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2020 Vol. 40, No. 09 Published: 2020-09-01

	2657	Application of White Noise Perturbation in Wavelength Modulated Off-Axis Integrated Cavity Spectroscopy
		WANG Jing-jing^{1, 2}, DONG Yang², TIAN Xing², CHEN Jia-jin², TAN Tu², ZHU Gong-dong², MEI Jiao-xu², GAO Xiao-ming^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2020)09-2657-07
		Off-axis integrated cavity output spectroscopy is an important method for trace gas detection, and its measurement limit is affected by residual cavity mode noise and background noise. In this paper, the residual mode noise in the output spectrum of OA-ICOS is reduced by injecting radio frequency (RF) white noise into the modulation current of the laser. Meanwhile, the influence of background signal is suppressed by wavelength modulation technology, and the signal-to-noise ratio of the OA-ICOS based methane sensor is further improved. Firstly, the influence of RF white noise with different power on the absorption spectra of methane in air is studied in detail, and the corresponding optimal modulation amplitude is calculated thought analyzing the linewidth of the absorption spectra. Subsequently, the influence of RF white noise with different power on 2f signal is studied. The results show that the baseline noise and 2f signal amplitude decrease simultaneously with the increase of RF noise power. By analyzing the signal-to-noise ratio of several sets of 2f signals, it is determined that the best power of RF white noise to improve the signal-to-noise ratio of the system is -25 dBm. Then, the relationship between methane concentration and the 2f signal in different concentration ranges was studied. The results show that the methane concentration is linear with 2f amplitude in the concentration range of less than 1.0×10^-6, and the methane concentration is in a curve relationship with the 2f signal in the concentration range of 0.1~2.2×10^-6. Subsequently, the stability of the system was investigated by measuring 2.2×10^-6 methane for a long time and Allan analysis of variance. The system has good stability, the optimal integration time is up to 1 250 s, and the corresponding minimum detectable limit is 1.2×10^-9. Finally, the methane concentration in the air out our laboratory was measured by using the built methane gas sensor for two days and nights. The results show that the diurnal variation of methane concentration is falling during the day and rising at night. The fluctuation range of methane concentration is 2.02×10^-6~2.3×10^-6, and the average concentration is 2.14×10^-6. This study provides a certain reference value for the application of off-axis integral cavity output spectroscopy in the measurement of the trace gas. It is of great significance for the development of high-precision in-situ trace gas measuring instruments.
		2020 Vol. 40 (09): 2657-2663 [Abstract] ( 218 ) RICH HTML PDF (4173 KB) ( 118 )

	2664	Research on Detecting CO₂ With Off-Beam Quartz-Enhanced Photoacoustic Spectroscopy at 2.004 μm
		XIE Ying-chao^1,2, WANG Rui-feng^1,2, CAO Yuan^1,2, LIU Kun^1*, GAO Xiao-ming^1,2
		DOI: 10.3964/j.issn.1000-0593(2020)09-2664-06
		CO₂ is an important component of the atmosphere and a product of excessive combustion of coal, oil, and natural gas in modern industrial societies. On the one hand, elevated concentrations of CO₂ in the atmosphere can cause a greenhouse effect, which is mainly due to human activities. On the other hand, CO₂ is a suffocating gas, and excessive accumulation of CO₂ in a closed environment can lead to safety problems such as suffocation. Therefore, the development of miniaturized and highly sensitive CO₂ detection technology has important significance and application requirements in the detection of atmospheric environment and safety monitoring of closed environmental work areas. In this paper, based on the rapid development of miniaturized quartz-enhanced photoacoustic spectroscopy technology, the research on CO₂ detection has been carried out with a relatively simple off-beam structure scheme. Off-beam quartz-enhanced photoacoustic spectroscopy has the advantages of small size, high sensitivity, anti-interference, low cost, low power consumption and low laser requirements, and has great potential for developing low-power portable gas sensors. In recent years, especially with the gradual maturity of near-infrared laser technology, it provides better quality and higher energy excitation light source for this technology, Off-beam quartz-enhanced photoacoustic spectroscopy has a higher detection sensitivity and enables accurate detection of low concentration gases. The HITRAN database 2012 is used to screen out the suitable absorption line (at 4 989.97 cm^-1), and the 2.004 μm near-infrared distributed feedback semiconductor laser is selected as the excitation source. The CO₂ photoacoustic signal is excited by the wavelength modulation method, and the second harmonic detection technology is used to detect the photoacoustic signal. In the experiment, the detection performance is improved by humidifying the injected carbon dioxide gas and optimizing the modulation amplitude, and the detection of air CO₂ is realized. Under normal pressure, different concentrations of CO₂ samples are arranged by gas distribution machine, and the response characteristics of concentration and signal are studied by preparing different concentrations of CO₂ samples thought gas distribution machine, and a good linear response result is obtained. At the same time, the signal offixed concentration of CO₂ sample under different pressures is measured, and Allan variance was used to evaluate the system performance. The results show that when the average time is 1 000 s, the detection limit of the system is 4×10^-3 μL·L^-1. The best 2f signal is obtained at a pressure of 150 Torr. The minimum detection sensitivity of the system for CO₂ is 15 μL·L^-1 at atmospheric pressure, and it is reduced to 6 μL·L^-1 at 150 Torr.
		2020 Vol. 40 (09): 2664-2669 [Abstract] ( 167 ) RICH HTML PDF (2767 KB) ( 74 )

	2670	Extrinsic Parameters Calibration of Ultra-Wide Angle Long-Wave Infrared Stereo Vision and Evaluation of Intrinsic and Extrinsic Parameters
		WANG Zi-ang, LI Gang, LIU Bing-qi, HUANG Fu-yu^*, CHEN Yi-chao
		DOI: 10.3964/j.issn.1000-0593(2020)09-2670-06
		In the infrared spectrum, the atmosphere has different transmittances for different wavelengths, and the range of the higher transmittance is called the atmospheric window. In order to detect the radiation of the target in the long-wave infrared spectrum in the large angle of view, and to make up for the defect that the conventional visible light camera cannot detect the target in a complex environment, an ultra-wide-angle long-wave infrared camera emerges. Compared with the traditional visible light camera, ultra-wide-angle long-wave infrared camera covers a large field of view and can be used in complex environments such as night-time and smoke, and has a certain penetration effect. The binocular ultra-wide-angle long-wave infrared the stereo vision can be used for vehicle night-time assisted driving, military unmanned combat platform all-weather information reconnaissance and the like. As the first step to realize the stereo vision, the accuracy of calibration seriously affects the accuracy of three-dimensional reconstruction of objects in stereo vision. Therefore, improving calibration accuracy is a key issue in stereo vision research. The purpose of calibration is to find the intrinsic parameters and extrinsic parameters of stereo vision imaging. The intrinsic parameters describe the image relationship of the camera lens imaging, and the extrinsic parameters represent the relative positional relationship between the two cameras. However, ultra-wide-angle long-wave infrared camera has severe imaging distortion, low resolution and low image contrast, which is extremely difficult for stereo vision calibration. In order to accurately calibrate the extrinsic parameters of ultra-wide-angle long-wave infrared stereo vision, this paper proposes an extrinsic parameter calibration method based on the least squares optimization based on the Scaramuzza universal camera model. Evaluate the accuracy of the intrinsic and extrinsic parameters, and the common monocular angle is used. Based on the intrinsic parameters of point projection error evaluation and introducing extrinsic parameters, a method for evaluating the binocular angle reprojection error is proposed. In order to verify the validity and accuracy of the method, the active infrared radiation calibration plate is used to generate the corner points, and two sets of binocular ultra-wide-angle long-wave infrared cameras with the field of view (FOV) of 180° and 210° are respectively used at different positions to do calibration experiments. The experimental results show that the commonly used Bouguet method has a binocular average reprojection error (BMRE) of 0.782～0.943 pixels, while the BMRE based on the least squares optimization method is at 0.620～0.754 pixels. The experimental data show that the proposed method effectively reduces the binocular angle. Projection error improves the accuracy of extrinsic parameter calibration. In addition, the evaluation method is simple, objective and accurate, avoids the three-dimensional reconstruction of the object point in the evaluation process to introduce additional errors, and does not require high-precision three-dimensional coordinate measuring equipment.
		2020 Vol. 40 (09): 2670-2675 [Abstract] ( 168 ) RICH HTML PDF (2299 KB) ( 63 )

	2676	The Infrared Unfolding State Sensing System of GF-7 Camera Baffle
		LIU Jing-lei^{1, 2}, FENG Hao¹, CAO Xu¹, JIANG Chang-hong¹, JIA He¹, ZHANG Zhang¹
		DOI: 10.3964/j.issn.1000-0593(2020)09-2676-05
		The resolution of space remote sensing satellites is getting finer and finer, and the aperture is getting large and large, which is restricted by the diameter of the carrier. So the baffle system of flexible, deployment has been applied to remote sensors gradually. The monitoring of the geometry state of the space flexible deployable structure in orbit plays an important role in the determination of the main mission. This paper introduces an infrared unfolding state sensing system which is applied to the in-orbit unfolding state monitoring of GF-7 camera baffle system. Firstly, this paper introduces the design of GF-7 camera baffle system and analyzes the special function of infrared sensingduring the unfolding state. A novel unfolded state perception method based on infrared ray transmission barrier characteristics by using lenticular boom as the transmission channel is proposed. The system includes the infrared ray generation system and receiving system, and the 890 nm wavelength light generated by the infrared ray generation system using as the data carrier. The data passing through the flexible deployable structure is converted into an electrical signal by the infrared ray receiving system and the flexible deployment unfolding state information is obtained. The system has the advantages of no moving parts, low power consumption and data bandwidth requirements, and has no mechanical impact on the deployment process. The feasibility of this method is proved through theoretical analysis, and an experiment is designed to verify the optical power of the infrared ray generation system in this method. According to the experiment, the optical power of the infrared system is greater than 25 mA, and the pull-up resistance of the extremely ray receiving system is greater than 15 kΩ, which is the ideal bound for the system. The infrared unfolding state sensing system can effectively determine the state under 125 ℃ when the pull-up resistance is no more than 50 kΩ. For the monitoring design and the application strategy in the environment of in-orbit of the same type of the flexible deployment baffle system, it provides the basis of test data and has a board application prospect.
		2020 Vol. 40 (09): 2676-2680 [Abstract] ( 157 ) RICH HTML PDF (2046 KB) ( 54 )

	2681	Ion Mobility Spectrometry Spectrum Reconstruction and Characteristic Peaks Extraction Algorithm Research
		ZHANG Gen-wei¹, PENG Si-long^{2, 3}, GUO Teng-xiao¹，YANG Jie¹, YANG Jun-chao¹, ZHANG Xu¹, CAO Shu-ya^1, HUANG Qi-bin^1
		DOI: 10.3964/j.issn.1000-0593(2020)09-2681-05
		Ion mobility spectrometry (IMS) is a rapid, highly sensitive analytical method for the gaseous samples with a low detection limit. It is widely used to detect chemical warfare agents, illegal drugs and explosives. The original spectrum contains not only sample information, but also noise. Especially when the concentration of the analyte is low, the accuracy of qualitative and quantitative analysis based on IMS technology is seriously influenced. It is necessary to reconstruct the spectrum before qualitative and quantitative analysis. In our article, a new method simultaneously achieved the spectrum reconstruction, and characteristic peaks extraction was proposed. In the optimization function, we chose l₁ norm as the linear penalty. The regularization parameter λ was used to adjust the scale of the penalty in the optimization. Solve the optimization function, a Gaussian dictionary was constructed to represent the shape of peak firstly, and the surrogate function algorithm was adopted to solve it. When the root mean squared error between the reconstructed and original spectrum achieved the set threshold, the algorithm was stopped. To evaluate the performance of our method proposed, the simulated data set and DMMP sample data set were used. The simulated data set was composed of Gaussian functions and Gaussian noise. Meanwhile, we compared our method with wavelet using a soft threshold, wavelet using hard threshold and S-G smoothing methods. Root mean squared error(RMSE) and signal to noise ratio(SNR) were used to compare the results of different methods. The experiments results show that our method has significant improvement than other methods. Based on the proposed method, qualitative and quantitative analysis can be carried out.
		2020 Vol. 40 (09): 2681-2685 [Abstract] ( 218 ) RICH HTML PDF (3449 KB) ( 118 )

	2686	Progress in Terahertz Imaging Technology
		CAO Bing-hua¹，LI Su-zhen^1*，CAI En-ze¹，FAN Meng-bao²，GAN Fang-xin¹
		DOI: 10.3964/j.issn.1000-0593(2020)09-2686-10
		In recent years, with the rapid development of photonics technology and microelectronic technology, the application fields of terahertz imaging has become more and more extensive, and have achieved some nice achievements in the fields of medicine and food monitoring, biomedical imaging, non-contact and non-destructive testing of devices, as well as the research on relics and work of arts,etc. This paper summarizes the superior performance of terahertz waves in imaging compared to microwaves and x-rays. Then, from the perspective of the light source, the terahertz continuous wave imaging and the terahertz pulse wave imaging are compared, including the system principle, imaging characteristics and their advantages and disadvantages. For the development of THz imaging technology in the past 20 years, this paper is aimed to introduce the four imaging methods including THz-TDS, focal plane array detection, near-field and compressed sensing. Their basic principles and development trends are also included.Among them, the terahertz time-domain spectroscopy technology takes the classical reflective spectral imaging system as an example, focusing on the basic principles of spectral imaging, the development trend of the technology and its practical cases in the pharmaceutical field. Moreover, on this basis, the terahertz source and the corresponding detector are introduced. In the part of focal plane array detection imaging technology, it consists of three kinds of current mature array cameras, which include CCD type, Microbolometer type and CMOS type. Near-field imaging technology is divided into two major imaging types. One is aperture imaging, and another is tip-scattering. The former summarizes typical near-field aperture illumination modes and collection mode. The latter includeslaser terahertz emission microscope and scanning tunneling microscope. The content covers the basic principles of system, the progress of current research, and the problems that remain in the development of the technology.The last part introduces an imaging method called compressed sensing, which can reduce the sampling rate of the system. It compares with the raster scanning sampling method used in terahertz spectral imaging technology. Furthermore, sincethe optical modulator is the key component of subsampling, its improved optimization process is analyzed. Improved algorithmsare also included.
		2020 Vol. 40 (09): 2686-2695 [Abstract] ( 267 ) RICH HTML PDF (6932 KB) ( 223 )

	2696	Application of Terahertz Spectroscopy in the Detection of Bioactive Peptides
		WANG Pu¹, HE Ming-xia^1*, LI Meng², QU Qiu-hong², LIU Rui³, CHEN Yong-de⁴
		DOI: 10.3964/j.issn.1000-0593(2020)09-2696-06
		Bioactive peptides, as the new darling of human health in the 21st century, have been proved that they have a good effect on human life activities, and their detection methods are also of great concern. Terahertz time-domain spectroscopy technology has incomparable advantage in detecting bioactive peptides because of its unique properties. In this paper, three bioactive peptides, bovine bone peptide, sea cucumber peptide and fish peptide, were used to obtain the absorption coefficient curve of 0.5~2 THz by the transmission terahertz time domain spectroscopy system. From the terahertz absorption coefficient curve, the absorption coefficient of the fish peptide is higher than that of sea cucumber peptide and fish bone peptide. Because of the interaction between the amino acid species of bioactive peptides and peptide bonds, there is no obvious absorption peak in the terahertz frequency band. In order to better detect and distinguish them, a classification discriminant model is established to find the most suitable for such substances. After the S-G smoothing and normalization preprocess performed on the terahertz original absorption coefficient data, two-thirds of the pre-processed data are randomly selected into training sets, and the rest are prediction set. The classification discriminant model is introduced. The model includes two parts: the classifier and the optimal parameter selection. The classifier selects the supervised classification method such as support vector machine, random forest and extreme learning machine, and uses the intelligent optimization algorithm such as genetic algorithm, particle swarm optimization and grid search to select the support vector machine optimal parameters. In order to reduce the original spectral data dimension and improve the computational speed of the model, Principal Component Analysis is used for preprocessing, and the results after dimensionality reduction are imported into the classification model. Considering the factors such as accuracy and running time, although the support vector machine based on particle swarm optimization has the highest accuracy rate of 98.3%, the running time is longer than 180 seconds; the ultimate learning machine can have the shortest running time of 0.2 seconds. However, the accuracy rate is 73.3%. The support vector machine based on grid search has an accuracy rate of 96% and a running time of 11 seconds. It can use a shorter time in the case of higher accuracy, and proves that the support vector machine based on grid search is better for detecting bioactive peptide. The results show that the use of terahertz time-domain spectroscopy combined with machine learning algorithms can achieve rapid and non-destructive detection of bioactive peptides, providing a new idea for the detection of bioactive peptides. It also demonstrates that THz-TDS combined with machine learning is a way better way for the identification of inconspicuous peptides.
		2020 Vol. 40 (09): 2696-2701 [Abstract] ( 220 ) RICH HTML PDF (3715 KB) ( 79 )

	2702	Terahertz Absorption and Molecular Vibration Characteristics of PA66 Polymer Material
		WANG Wen, QIU Gui-hua^*, PAN Shi-bing, ZHANG Rui-rong, HAN Jian-long, WANG Yi-ke, GUO Yu, YU Ming-xun
		DOI: 10.3964/j.issn.1000-0593(2020)09-2702-05
		Polymer materials have peculiar fingerprint spectrum in terahertz (THz) band, which has a potential application in the field of materials feature recognition and detection. In this study, the absorption spectra of PA66 in the THz region was studied by using terahertz time domain spectroscopy. The refractive index and dielectric constants were measured and analyzed. And the THz absorption spectra of PA66 were obtained between 0.2 and 2.3 THz. Then the vibration frequencies of PA66 were calculated using density functional theory (DFT) in the range of 0.1~10 THz. The results show that the calculated vibration frequencies of PA66 are in good agreement with experimental absorption spectra. By analyzing the vibration modes of the molecular, the absorption peaks within 0. 2~2. 3THz are generated from the oscillation of —C═O, —NH groups and the asymmetric motions of —CH₂ in the backbone. And the vibration peak at 0.77 THz is generated from an out-of-plane wagging of —C═O, —NH groups, the 1.56 THz band is due to a combination of an out-of-plane wagging of —C═O, —NH groups and a rotor motion of the —CH₂ group, while the vibration peak at 1.85 THz is due to an out-of-plane oscillation of —C═O, —CH₂ groups, which come from adipic acid. The band centered near 4.57 THz that represents several modes associated with an in-plane wagging of the —C═O groups and a rotor motion of —CH₂ groups from adipic acid, and the 7.6 THz band are associated with a wagging of —C═O and a scissor motion of —CH₂, —NH groups. The results show that the absorption features in the THz regime of polymer materials is associated with the vibration motions of various groups in molecules, and the absorption peaks are generally due to the motions of wagging, rotor, oscillation and the intermolecular interactions. In addition, the asymmetric polar polymer material with N, O atoms is apt to generate dipole moments for the difference in electronegativity. Therefore, it is easy to show fingerprint characteristic peaks in the THz band, which can provide the theoretical basis and technical support for the structural analysis and identification of materials using THz technology.
		2020 Vol. 40 (09): 2702-2706 [Abstract] ( 152 ) RICH HTML PDF (3010 KB) ( 63 )

	2707	Terahertz and Raman Spectra of EDTA-2Na
		LU Mei-hong¹, GONG Peng², ZHANG Fan¹, WANG Zhi-jun¹, FENG Duo¹，MENG Tian-hua³
		DOI: 10.3964/j.issn.1000-0593(2020)09-2707-06
		Disodium ethylenediaminetetraacetate (EDTA-2Na) is a chelating agent containing carboxyl and amino groups, which has a wide range of coordination properties. EDTA-2Na is often widely added to food as a color protection agent, quality improver and synergistic antioxidant. However, the excessive or improper use of EDTA-2Na will harm human health; even cause temporary blood pressure drop, kidney disorders, etc. Therefore, it is necessary to propose a rapid method of food EDTA-2Na detection. The process of the traditional detection method is complex, time-consuming and labor-consuming. THz time-domain spectroscopy and Raman spectroscopy have good fingerprint characteristics for EDTA-2Na. The advantages of safe and fast detection make it have great application potential. At present, the theoretical and experimental study of EDTA-2Na detection by THz-TDS and Raman spectroscopy has not been reported. EDTA-2Na crystalline powder was detected for the first timer by using THz time domain spectroscopy and Raman spectroscopy. The characteristic absorption and Raman scattering spectra of EDTA-2Na crystal powder in 0.2～2.6 THz band and 10～4 000 cm^-1 band were obtained. Based on density functional theory, the vibration frequency of EDTA-2Na molecule was optimized and calculated by B3LYP/6-31G* group. The corresponding vibration modes were assigned and analyzed. The results showed that there is obvious terahertz vibration absorption at 0.88, 1.40, 1.73 and 2.32 THz for EDTA-2Na, which is basically consistent with the abnormal dispersion frequency of the refractive index spectrum. Moreover, there are obvious Raman characteristic peaks at 921，963，990，1 081，1 336，1 428 and 1 614 cm^-1, which are in good agreement with the experimental results and can be used as the characteristic peaks for identification and detection. In particular, the low-frequency Raman spectrum and THz spectrum in the range of 6.7～85.8 cm^-1 (0.2～2.6 THz) were compared, and the mechanism of characteristic peaks generation was analyzed with density functional theory. The results showed that there is a strong complementarily and consistency between THz spectrum and low-frequency Raman spectrum, which can be used as an effective complementary means of spectrum technology to detect EDTA-2Na, and the results were reliable. The study provides experimental reference and theoretical basis for the detection of food additive EDTA-2Na and the establishment of the database.
		2020 Vol. 40 (09): 2707-2712 [Abstract] ( 195 ) RICH HTML PDF (2707 KB) ( 103 )

	2713	Research Progress on Non-Destructive Detection Technology for Grape Quality
		SUN Jing-tao^1,3，LUO Yi-jia¹, SHI Xue-wei¹，MA Ben-xue²，WANG Wen-xia²，DONG Juan^1*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2713-08
		Grape is rich in nutrients and has therapeutic effects, making it one of the most popular fruits for consumers. However, the quality of grape degrades due to their vulnerability in the process of growth, picking and storage, which seriously affects the purchasing desire of consumers and the selling price of grape. Therefore, detecting grape quality is crucial for improving the commercial value of grape. Traditional testing methods have the disadvantages of destroying samples, time-consuming and labor-intensive, high cost. However, non-destructive testing methods, which mainly adopt machine vision technology, near-infrared spectroscopy technology and high spectral imaging technology, have developed rapidly due to their advantages of non-destructive, rapid and accurate, and have formed a relatively perfect method system. At present, non-destructive testing technology is widely used in grape quality testing. The national and international latest researches of using machine vision, near-infrared spectroscopy and hyperspectral imaging technology in the detection of grape, including prediction of external quality (fruit size, surface color and bunch size) and internal quality (varieties, sugar, titratable acid, anthocyanins and total phenols, diseases and pesticide residues, etc.), were summarized. Finally, the existing problems and the prospects were analyzed, which provide a reference for the development of non-destructive testing technology of grape and the work of related scientific researchers.
		2020 Vol. 40 (09): 2713-2720 [Abstract] ( 228 ) RICH HTML PDF (2800 KB) ( 130 )

	2721	The Silver Surface Plasmon Enhancement for Er³⁺ Ion Upconversion of 978 and 1 539 nm Laser in Bismuth Glass
		CHEN Xiao-bo¹, LI Song¹, ZHAO Guo-ying², LONG Jiang-mi¹, WANG Shui-feng¹, MENG Shao-hua², WANG Jie-liang¹, GUO Jing-hua¹, YOU Jia-jia¹, MA Yu², YU Chun-lei³, HU Li-li³
		DOI: 10.3964/j.issn.1000-0593(2020)09-2721-06
		Upconversion luminescence of trivalent rare-earth ions has some valuable application technologies: Waveguide upconversion and amplification and laser, Up-conversion three-dimensional display, Femtosecond spectral applications, Laser temperature control, Three-dimensional imaging and storage, Optical temperature sensing system, Dental and other biophysical applications, Up-conversion fluorescence anti-counterfeiting, Up-conversion broadband light source, and Up-conversion infrared display, etc.Promoted by the demand for solar cells, up-conversion research has once again shown a surging upsurge of research. Current, using the near field enhancement effect of metal surface Plasmon resonance can effectively enhance the luminescent properties of fluorescent substances near its surface. It is possible to increase the intensity of upconversion luminescence by a considerable margin. Thus, it is possible to promote up-conversion luminescence to practical use further. We use the ion introduction method to introduce silver particles into bismuth luminescent glass. Experimental results show that the surface Plasmon resonance absorption peak of silver surface Plasmon is positioned at about 580～600 nm. Moreover, the prolongation of heating time leads to a sharp enhancement and a slight blue shift of the surface Plasmon absorption peak. Subsequently, we find that three groups two-photon upconversion fluorescence of 531.0 nm ²H_11/2→⁴I_15/2, 546.0 nm ⁴S_3/2→⁴I_15/2, and 657.5 nm ⁴F_9/2→⁴I_15/2of Er³⁺ ions can be induced by the 978 nm laser. Upconversion luminescence mechanism, when erbium doped bismuth luminescent glass excited by 978 nm laser, is the first step ⁴I_15/2→⁴I_11/2resonance ground state absorption and the second step ⁴I_11/2→⁴F_7/2 resonance excited state absorption. The introduction of silver nano surface Plasmon facilitates that the maximum enhancement of upconversion luminescence is 272.0% times bigger in bismuth glass, when excited by 978 nm laser. Finally, the 1 539 nm laser can induce four groups upconversion fluorescence of 528.0 nm ²H_11/2→⁴I_15/2, 547.0 nm ⁴S_3/2→⁴I_15/2, 657.0 nm ⁴F_9/2→⁴I_15/2, and 795.0 nm ⁴I_9/2→⁴I_15/2 of Er³⁺ ion. The mechanism of 528.0 nm ²H_11/2→⁴I_15/2 and 547.0 nm ⁴S_3/2→⁴I_15/2 upconversion luminescence is mainly ⁴I_15/2→⁴I_13/2, ⁴I_13/2→⁴I_9/2, and ⁴I_9/2→²H_11/2three-step photo-excited absorption transition process of 1539 nm laser. The mechanism of 657.0 nm ⁴F_9/2→⁴I_15/2 upconversion fluorescence is mainly the ⁴I_15/2→⁴I_13/2, ⁴I_13/2→⁴I_9/2, and ⁴I_11/2→⁴F_9/2 three-step photo-excited absorption transition process of 1 539 nm laser. Furthermore, the introduction of silver nano surface Plasmon induces that the maximum enhancement of up-conversion luminescenceof Er³⁺ ionstimulated by 1 539 nm laser is 160.3% times bigger in bismuth glass. Obviously, the enhancement effect of 978 nm laser upconversion, which is near the resonance absorption peak of silver surface Plasmon, is better than that of 1 539 nm laser upconversion.
		2020 Vol. 40 (09): 2721-2726 [Abstract] ( 158 ) RICH HTML PDF (2586 KB) ( 49 )

	2727	Investigation of Carrier Recombination Dynamics of Light-Emitting Diode Based on InGaN Quantum Dots
		CAO Jie-hua^{1, 2}, TIAN Ming^{1, 2}, LIN Tao^{1, 2*}, FENG Zhe-chuan^{1, 2}
		DOI: 10.3964/j.issn.1000-0593(2020)09-2727-05
		InGaN semiconductor materials are widely used In a new generation of optoelectronic devices because of their adjustable bandgap width by changing In components. However,the green LED still has a “green gap” problem to be solved. In this paper, the carrier recombination mechanism is studied in depth to provide a new idea for solving a “green gap”. The photoluminescence spectrum (PL) and time-resolved photoluminescence spectrum (TRPL) were used for investigating the carrier recombination processes of InGaN quantum dots (QDs) LED devices with different photon energies at temperatures. The transient photoluminescence properties of InGaN QDs and the transient life of radiative/nonradiative recombination were obtained. In the temperature range from 15 to 300 K, the peak value of the steady-state photoluminescence spectrum has its first blue shift and then red shift (s-shaped). The blue shift of the emission peak is about 4.2 meV, reaching its maximum value at 60 K, followed by the red shift of the emission peak, forming an s-shaped change with temperature. This change indicates that carrier localization behavior in QDs structure, and exciton recombination is the main reason for green light emission of InGaN QDs. By fitting the normalized PL integral intensity at different temperatures, the activation energy E_act was about 204.07 meV, with high activation energy, which proved that the InGaN QDs have strong carrier limiting effect and can better suppress the transitions to the nonradiative recombination centers. The internal quantum efficiency was estimated at 35.1%. Free carrier in the InGaN QDs composite average composite life τ_rad=73.85 ns. The energy boundary value E_me=2.34 eV is much higher than the local depth E₀=62.55 meV, and it can be seen that the energy level is completely lower than the mobility edge, so the decay of InGaN QDs life is attributed to carrier local state recombination. In this study, the improved spectral data analysis method was used to study the fluorescence device based on the new structure of embedded QDs, and meaningful conclusions were obtained. It provides a reference for further understanding of the internal luminescence mechanism of InGaN quantum dots and the development of a new generation of lighting devices, indicating that the introduction of InGaN quantum dots plays a good role in promoting the development of photoelectric devices.
		2020 Vol. 40 (09): 2727-2731 [Abstract] ( 183 ) RICH HTML PDF (2549 KB) ( 60 )

	2732	Rapid Quantitative Analysis of Methamphetamine by Near Infrared Spectroscopy
		LIU Cui-mei¹, HAN Yu¹, JIA Wei¹, HUA Zhen-dong¹, MIN Shun-geng^2*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2732-05
		In this study, a near infrared partial least squares (NIR-PLS) quantitative model, which involved seven adulterants and with methamphetamine purity ranging from 10% to 100%, was established for the first time. Seven adulterants of dimethyl sulfone, isopropyl benzylamine, sucrose, cyclohexylamine, aluminum potassium sulfate, piracetam and ephedrine were most frequently detected in seized methamphetamine samples. High purity methamphetamine and adulterants were mixed to prepare the model samples to make sure the established quantitative model can cover the common adulterant species and purity range of actual seized samples. The characteristic absorption peaks of methamphetamine and adulterants occur in different spectrum range, so the whole spectrum range was used for the PLS modeling. The standard normal variate transformation+first-order derivative (SNV+1D) was proved to be the best spectral pretreatment method. Two separate PLS quantitative models were established to improve the accuracy of the models. The PLS factor, coefficient of determination (R²), root mean square error of cross validation (RMSECV), and root mean square error of prediction (RMSEP) for model 1 was 8, 99.9, 0.8%, and 2.0%, respectively. Model 1 is suitable for high purity methamphetamine samples without adulterant and methamphetamine samples adulterated with dimethyl sulfone, isopropyl benzylamine, sucrose, and cyclohexylamine. The PLS factor, R², RMSECV, and RMSEP for model 2 was 5, 99.9, 0.8%, and 1.7%, respectively. Model 2 was suitable for methamphetamine samples adulterated with aluminum potassium sulfate, ephedrine, and piracetam. The repeatability and reproducibility for both models were less than 2.1% and 4.0%, respectively. Seventy-two seized methamphetamine samples with purity ranging from 13.9% to 99.4% were used to validate the accuracy of the two models. The average purity determined by liquid chromatography and near infrared spectroscopy was 74.3% and 72.9%, respectively. The t-statistics values were 3.0, which was higher than the significant level of 0.05, so it showed that there was no significant difference between the two methods. Mahalanobis distance and spectral residual were selected as the outlier identification methods. When the Mahalanobis distance value is less than 2, and the spectral residual value is less than 3, the quantitative result is reliable. On the contrary, the quantitative result is unreliable, and the other method is needed for quantitative analysis. The established NIR-PLS method is simple in sample preparation, fast in testing, accurate in quantitative results and high in accuracy. It is suitable for rapid quantitative analysis of methamphetamine in seized samples. The sampling and modeling methods involved in this study are also applicable to other drugs.
		2020 Vol. 40 (09): 2732-2736 [Abstract] ( 226 ) RICH HTML PDF (2116 KB) ( 85 )

	2737	Near Infrared Spectral Characteristics and Qualitative Analysis of Typical Coal-Rock Under Different Detection Distances and Angles
		ZHOU Yue, WANG Shi-bo^*, GE Shi-rong, WANG Sai-ya, XIANG Yang, YANG En, LÜ Yuan-bo
		DOI: 10.3964/j.issn.1000-0593(2020)09-2737-06
		The reflection spectrum of the near-infrared band was convenient to measure. It was not necessary to pretreat the sample, but also suitable for on-line analysis. In order to realize the identification of coal-rock in the automatic coal caving technology of fully mechanized caving mining based on the near-infrared spectroscopy, the fully mechanized caving work from a mine four kinds of typical massive coal-rock samples such as carbonaceous mudstone, sandy mudstone, sandstone and gas coal were collected, and the coal pile condition of the rear scraper conveyor was considered comprehensively. Near infrared diffuse reflectance spectra of four typical coal-rock with common detection distances (1.3, 1.4, 1.5 m) and detection angles (10, 20, 30, 40 and 90 degrees) were collected in the laboratory by the spectrometer. By analyzing the spectral characteristics of four typical coal-rock, it was found that the detection angle and distance have no significant influence on the spectral curve and the position of the absorption valley, but obviously affected the reflectivity of the spectral curve. Carbonaceous mudstone，sandy mudstone and sandstone all have obvious absorption valleys near the 1 400, 1 900 and 2 200 nm bands. In addition, sandstone and carbonaceous mudstones have double absorption valleys near the 2 200 nm band. The diffuse reflectance spectral curve of coal in the near-infrared region is generally horizontal, with no obvious absorption valley. At the detection distance of 1.3 m, the reflectance of the spectral curve increased with the increased of the detection angle; at the detection distance of 1.4 and 1.5 m, the reflectance of the spectral curve decreased with the increased of the detection angle. At the detection angles of 10°, 20° and 30°, the reflectivity of the spectral curve increased with the increased of the detection distance; at the detection angle of 40° and 90°, the reflectivity of the spectral curve increased with the detection distance. Three methods of first-order differential (FD), Savitzky-Golay convolution smoothing (SG convolution smoothing), and standard normal enthalpy switching (SNV) were used to enhanced spectral absorption characteristics and eliminated detection conditions for coal-rock diffuse reflection spectra. The effect of SG convolution smoothing on the premise of enhancing spectral absorption characteristics also effectively eliminated the influence of detection angle and height on the spectral curve. The qualitative analysis of coal-rock was carried out by using two models of cosine similarity and Pearson correlation coefficient. The results showed that the cosine similarity model based on S-G convolution smoothing was the best, and the correct classification rate was 100%. Obtaining the best pre-processing method and qualitative analysis model can provide reference for the rapid and qualitative identification of coal-rock by directly using the waveform of the reflection spectrum at different detection distances and detection angles.
		2020 Vol. 40 (09): 2737-2742 [Abstract] ( 194 ) RICH HTML PDF (3821 KB) ( 73 )

	2743	Spectral Characteristics of Ag₃PO₄/GO on Nickel Foam and Photocatalytic Degradation of Ethylene Under Visible Light
		JI Bang^1,2, ZHAO Wen-feng³, DUAN Jie-li⁴, FU Lan-hui¹, MA Li-zhe³, YANG Zhou^1*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2743-08
		Ethylene released during storage and transportation of fruits and vegetables is the main factor for post-harvest spoilage of fruits and vegetables. Therefore, how to reduce or remove the ethylene released during the storage and transportation of fruits and vegetables has become an urgent problem to be solved. Photocatalytic oxidation technology is widely used in air purification, sewage treatment and energy fields due to its characteristics of energy saving, environmental protection and pollution-free. In this paper, photocatalytic degradation of organic pollutants was used to prepare a thin film photocatalyst and used it for photocatalytic degradation of ethylene. A series of metal nickel foam supported silver phosphate/graphene oxide (Ag₃PO₄/GO) composites film was prepared by using the silver nitrate as the silver source and the three-dimensional metal nickel foam as the carrier. The samples were characterized by X-ray diffraction (XRD), Ultraviolet-visible absorption spectroscopy (UV-Vis), Raman spectroscopy (Raman), Scanning electron microscopy (SEM), Fluorescence spectroscopy (PL), X-ray photoelectron spectroscopy (XPS). The results show that the pore structure is formed on the surface of the nickel foamed after corrosion oxidation treatment, which provides more adhesion sites for the catalyst and is favorable for catalyst adhesion. The Ag₃PO₄/GO film was successfully loaded on the surface of the 3D metal nickel foam, and the addition of GO did not change the crystal structure of Ag₃PO₄. After adding GO into Ag₃PO₄, the absorbance in the visible light region changed significantly. As the amount of GO increased, the absorbance of the sample in the visible light region increased. The addition of GO inhibits the recombination of photogenerated electron-hole pairs, which was conducive to improving the photocatalytic performance. Taking ethylene as the degradation target, the effect of different GO mass percentage of Ag₃PO₄ films on photocatalytic degradation of ethylene under visible light was investigated. The kinetic analysis of the photocatalytic degradation process was also carried out. The results show that Ag₃PO₄/GO composite film exhibits good photocatalytic activity and cyclic stability under visible light, and the photocatalytic degradation of ethylene can be improved by adding appropriate GO. The sample AG/NF-2 (the mass percentage of GO is 2% of Ag₃PO₄) has the highest photocatalytic activity, and the photocatalytic rate constant is 1.72×10^-3 min^-1, which was 181.96% higher than that of pure Ag₃PO₄. The stability of the photocatalytic degradation of ethylene in AG/NF-2 samples was tested. The results show that the introduction of GO inhibits the photocatalytic corrosion of Ag₃PO₄ and the photocatalytic stability is stable. Finally, the mechanism of photocatalytic degradation of ethylene by nickel foam supported Ag₃PO₄/GO thin film was also proposed. This work will bring about a potential application of photocatalytic technology in the field of fruit and vegetable preservation.
		2020 Vol. 40 (09): 2743-2750 [Abstract] ( 148 ) RICH HTML PDF (5095 KB) ( 61 )

	2751	Surface-Enhanced Raman Spectroscopy Analysis on the Serum, Muscular and Synovial Tissue of the Knee in Knee Osteoarthritis Model Rats
		WANG Lu-lu¹, LIU Lei¹, LI Pan², WANG Jie^3,4, HE Lu^3,4, WU Zi-jian^3,4*, YANG Liang-bao², HU Ling⁴
		DOI: 10.3964/j.issn.1000-0593(2020)09-2751-05
		Surface-enhanced Raman Spectorscopy (SERS) spectra of serum, knee muscles and synovial tissues of normal rats and Model of Knee Osteoarthritis (KOA) are analyzed, providing an experimental basis for biological changes of KOA. Twenty normal healthy male SD rats were raised under the same conditions and randomly divided into normal control group (referred to as “normal group”) and KOA model group (referred to as “model group”), with 10 rats in each group. The KOA model was prepared by intra-articular injection of 0.03 mol·L^-1 of l-cysteine and 4% papain. The classification of surficial enhanced Raman spectra in serum, knee muscle and synovial tissue of rats was detected by silver nanometer substrate fluid. NGLabSpec software was used to compare the differences of Raman frequency shift and characteristic peaks between the two groups, and OriginPro 8.5 software was used to analyze Raman spectra. In the serum, within the range of Raman frequency shift 400～2 000 cm^-1, there were 12 characteristic peaks in the normal group and 14 in the model group, and the peak strength of most characteristic peaks in the model group was lower than that in the normal group, and significant difference characteristic peaks appeared in 495, 883 and 1 447 cm^-1 of the two groups; in the muscle tissue of the knee joint, there were 12 characteristic peaks in the normal group and 13 in the model group, there were significant differences in the Raman strength of the characteristic peaks of homogeneity between the two groups; in synovial tissues and the normal group has 10 characteristic peaks, has 15 model group, two groups of the common characteristic peak of peak intensity change is not obvious, but in the 655, 950, 1 335, 1 447 cm^-1 of the homogeneous characteristic peaks show that the peak strength of significant differences, in 655 and 950 cm^-1 of a significant rise in the model group, and 1 335 and 1 447 cm^-1 model is the relative strength of two groups decreased significantly. KOA model led to a significant decrease in the number of homogenous characteristic peaks of serum, knee muscle and synovial tissue, and an increase in different substances. Thus the metabolic balance of substances was seriously broken. SERS is a fast and accurate detection method, which can be used for the detection of KOA model.
		2020 Vol. 40 (09): 2751-2755 [Abstract] ( 168 ) RICH HTML PDF (3622 KB) ( 46 )

	2756	Development of a Novel Floating Water Spectral Measurement System Based on Skylight-Blocked Approach
		TIAN Li-qiao¹, LI Sen^1*, SUN Xiang-han¹, TONG Ru-qing¹, SONG Qing-jun², SUN Zhao-hua³, LI Yong¹
		DOI: 10.3964/j.issn.1000-0593(2020)09-2756-08
		It has been a long-standing and challenging goal to precisely measure water-leaving radiance (Lw) in ocean color remote sensing. Conventional approaches like in-water profile method, above-water method and other water measurement methods cannot directly measure Lw. Thus, they demand complex post-measurement processes, which cause many uncontrollable factors. Skylight-blocked approach (SBA), proposed by Zhongping Lee in 2013, was an innovative method to directly measure L_w, which can avoid uncertainties caused by post-measurement processes. However, no water spectral measurement system based on SBA has been developed so far. It is of great theoretical and practical significance to develop and test such equipment. Based on SBA, a water spectral measurement system was developed in this study. Firstly, the principles of the field water-spectrum measurement method, SBA is introduced. Then, the hardware structure and system design of the system are described in detail. The field experiment in Pearl River Estuary (113°32′38″E, 22°25′43″N) was carried out on September 20, 2017 to test the continuous measurement ability of the system. The system can measure Lw and downward irradiance of water (E_s) directly and synchronously and then calculated the R_rs. The coefficients of variation (CV) of them are less than 5%, which basically proves the effectiveness of the water spectral measurement system in L_w measurement. The measurements results of the system are in good agreement with those of Maya2000 Pro synchronous. The continuous observation experiments show the stability of measurements and the ability to track the change of optical characteristics of water. The existing problems and future development prospects are pointed out, such as self-shading correction, data quality control, high frequency measurement, buoy tilts recording, multi-factor joint measurement, long time series and large range networking, etc. To summarize, the water spectral measurement system based on SBA called the Floating Optical Buoy (FOBY) can measure L_w directly in high the frequency of minutes which can track the rapid dynamic change of water optical characteristics. It is expected to improve the matching efficiency between in-situ measurement data and satellite remote sensing data. Based on the system, big data sets of water spectral can be obtained by means of the network. It is conducive to greatly improve the application potential of various satellite data on ocean color remote sensing.
		2020 Vol. 40 (09): 2756-2763 [Abstract] ( 177 ) RICH HTML PDF (5570 KB) ( 58 )

	2764	Fabrication on Monolayer Film of (Au-Probe)@SiO₂ Nanoparticles and Its Surface Enhanced Raman Spectroscopic Investigation
		LIU Ke, ZHANG Chen-jie, XU Min-min, YAO Jian-lin^*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2764-06
		Surface-enhanced Raman spectroscopy (SERS) has been developed as a powerful tool in surface science due to its ultrahigh surface sensitivity up to the single molecular detection. The enhancement mechanisms include electromagnetic enhancement mechanism (EM) andchemical enhancement mechanism (CM). In general, the dominant contributor to most SERS processes is the EM, and the local electromagnetic field in the EM greatly enhance the surface Raman signal intensity of the adsorbed molecules. In addition, the medium has traditionally played a vital role in SERS measurements, as the medium also exhibits a certain influence on the local electromagnetic field as well as the EM enhancements. Shell-isolated nanoparticles (SHINs) can avoid direct contact between the medium and SERS enhancement source through the inert shell on the surface of the particles. However, up to now, few studies have been conducted on the effect of dielectrics on the shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS), which is often due to the poor homogeneity of SERS substrates. Herein, two core-shell nanostructures embedded with probe molecules were fabricated successfully, i. e. (55 nm Au-PNTP)@SiO₂ and (110 nm Au-pMBA)@SiO₂ with the shell thickness of about 3.5 and 4.0 nm, respectively. The continuous shell layer covered the Au core nanoparticles compactly without pinhole effect. The core-shell nanoparticles monolayer layer was assembled at the liquid-liquid interface, and it was transferred to flat solid surface as SERS substrate. The monolayer film of (55 nm Au-PNTP)@SiO₂ exhibited the uniform SERS effect with the relative standard deviation (RSD) of about 5.38%, while RSD of 5.97% for (110 nm Au-pMBA)@SiO₂. Therefore, the reasonable performance of the monolayer film allowed serving as qualified SERS substrate. The medium effect was explored on the monolayer film in the water and air system. It demonstrated that the pinhole free continuous shell and the probe embedded into the shell brought isolation of the EM source and the probes with the medium condition. Although the outside medium (condition) was changed from air to water, the real medium of the probes and the Au core was still surrounded by the SiO₂ shell. Consequently, the SERS signal intensity was independent of the outside medium. The probe molecules of PNTP and pMBA was embedded into the SiO₂ shell, and the SPR catalysis reaction was absent due to the isolation of oxygen and solvent. Thus the spectral feature of probes was quite stable. It indicated that no influence on the SERS effect was observed by different medium for the probes embedded nanostructures. It was expected to develop the probes embedded nanostructures as SERS substrate for the sensitive detection in a different medium, and as an internal standard for calibration of SERS effect.
		2020 Vol. 40 (09): 2764-2769 [Abstract] ( 201 ) RICH HTML PDF (3519 KB) ( 92 )

	2770	Preparation and Spectral Analysis of Modern Anti-Ultraviolet Cotton Fabrics
		ZHANG Jia-qin¹, FANG Zhi-hao², LIANG Hui-e^1*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2770-05
		Clothing functionalization is a research hotspot in the field of modern clothing, and the design and development of functional textiles are one of the important links. Strong UV light will cause skin sunburn and even canceration, especially the special population such as patients with albinism need ultraviolet protection. It is of great practical significance to develop UV resistant functional textiles. Graphene oxide has been widely used because of its excellent properties. It can be used in the development of conductive, antibacterial, UV resistant and other functional textile clothing. In this paper, graphene oxide is attached to cotton fabric fibers by adsorption and deposition methods, and the environmentally friendly reducing agent L-ascorbic acid is used to reduce it to obtain reduced graphene oxide UV-resistant cotton fabrics with different reduction degrees. The reduction degree of the reduced graphene oxide cotton fabric was characterized by reflectance curve, Raman spectrum and XPS. The results show that the reflectance of the reduced graphene oxide cotton fabric decreases with the increase of the reductant concentration after the reduction of graphene oxide on the surface of cotton fabric; the intensity of D and G peaks in the Raman spectrum changes obviously, the I_D/I_G value increases gradually with the increase of the reductant, and the reduction degree increases; the XPS spectrum shows that the carbon and oxygen content of the reduced graphene oxide cotton fabric increases before and after the reduction, the content of carbon increased, the content of oxygen decreased, and the ratio of carbon to oxygen increased from 2.26 to 2.90, indicating that the content of oxygen-containing functional groups decreased. Finally, the UV resistance of the reduced graphene oxide cotton fabric with different reduction degree was tested. When the concentration of L-ascorbic acid reducing agent was 7 and 10 mg·mL^-1, the UPF value of the reduced graphene oxide cotton fabric was between 100～120, and the UV transmittance was below 2%, which was significantly improved compared with the blank cotton fabric. The reduced graphene oxide cotton fabric studied in this paper can be used in the design and development of cotton textiles and clothing to meet the demand of clothing anti UV function, and it has great market potential and application prospect.
		2020 Vol. 40 (09): 2770-2774 [Abstract] ( 168 ) RICH HTML PDF (2783 KB) ( 50 )

	2775	A Comparative Study on the ATR and TR Methods of Infrared Spectroscopy of Solid Matters
		YANG Shan, CAI Xiu-qin, ZHANG Yi-feng
		DOI: 10.3964/j.issn.1000-0593(2020)09-2775-06
		Infrared (IR) spectroscopy is a common tool for material structure analysis, which is widely used in the detection of various solid, liquid and gaseous materials. Since the testing methods and sample preparation process directly affect the accuracy of IR spectra, the comparative study on the attenuated total reflection (ATR) and transmission (TR) methods used in measuring the IR spectroscopy of solid substances was carried out in this paper. Three kinds of common polymers, 3 kinds of inorganic substances, and 3 kinds of organic small-molecule compounds were selected as the research objects, and their IR spectra were measured by both ATR and TR methods, respectively. Various abnormal phenomena in the IR spectra were deeply analyzed combining sample preparation process and spectrum analysis. The TR method is complicated in the sample preparation process and has many interfering factors: the sample is easy to absorb moisture from the air during mixing and grinding process with KBr, which will disturb the analysis of samples having N—H and O—H groups; the spectrum will appear flat-headed peaks and can’t be analyzed as the sample is too thick, and the absorption intensity of the whole spectrum will decrease as the sample is dark colored. Compared with the TR method, the ATR method requires no sample preparation, which saves both time and labor and reduces the possibility of water absorption. Because the method principle is different, the overall absorption intensity of the ATR method is less than that of the TR method. Although the peak wavenumber of the ATR method is generally smaller than that of the TR method among several to dozens of cm^-1, the peak position is in a reasonable range, and the qualitative analysis is unaffected. Because the depth of light enters the sample is limited in the ATR method, only 2~15 μm, no flat-headed peaks appear in the ATR-IR spectrum. Due to the short wavelength light cannot penetrate the sample too deep, thus, the absorption intensity in ATR-IR spectrum will weaken along with the decrease of wavelength, which leads to the peak intensity in functional group region decrease and in fingerprint region greatly increases, while this problem can be revised by the “ATR correction” function of software, and also can be used in the quantitative analysis: the original spectra for analyzing the peaks in the fingerprint region, and the revised spectra for analyzing the peaks in functional group region. In comparison, the ATR method is not limited by the color, shape and thickness of the sample, i.e., the measurement is easier, fast, accurate, non-destructive, and recyclable. It has obvious advantages in the IR spectra detection of polymer, dark colored and hygroscopic substances. Thus, it is recommended to widely use the ATR method for detecting IR spectra of solid substances.
		2020 Vol. 40 (09): 2775-2780 [Abstract] ( 292 ) RICH HTML PDF (2830 KB) ( 159 )

	2781	The Categories of the UV-Vis Reflectance Spectra of Seawater Cultured Black Pearl and Its Unique PL Spectral Characteristics
		YAN Jun^1,2, SUN Qing², YAN Xue-jun¹, FANG Shi-bin¹, SHENG Jia-wei², ZHANG Jian^2*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2781-05
		The spectral characteristics of seawater cultured black pearl were systematically investigated by ultraviolet-visible (UV-Vis) reflection spectrometer combining with photoluminescence (PL) spectrometer. The results show that: (1) based on the intensity and presence of the absorption peaks located at around 400, 500 and 700 nm in the UV-Vis reflection spectra, the black pearls were firstly classified into four categories. Namely, firstly, most of the pearls have absorption at around 400, 500 and 700 nm. Secondly, a small number of pearls have absorption peaks at around about 400 and 500 nm. Thirdly, partly of pearls absorption appeared peaks at about 400 and 700 nm. Finally, the rest of the pearls in this work appeared absorption peak around 500 and 700 nm. (2) Under the condition of 405 nm excitation light source and the room temperature, the unique characteristic absorption was located at around 620, 653 and 677 nm in PL spectra of black pearls, which did not appear in the other kinds of pearls. Interestingly, of which about 677 nm absorption peak had a sensitive to ultraviolet light, namely with the extension of irradiation time, the intensity of the absorption peak becomes weak or even disappears. In a word, the technology UV-Vis reflection combined with PL spectrum will take an important role in the identification of black pearls.
		2020 Vol. 40 (09): 2781-2785 [Abstract] ( 172 ) RICH HTML PDF (4677 KB) ( 57 )

	2786	In Vivo Spectrofluorimetry of Polypeptide (GPG) Anti-Tumor Activity
		YE Ruo-bai¹, WU Zhen-hong², MIAO Xiao-qing^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2020)09-2786-05
		This paper explores the in vivo action of an anti-cancer polypeptide (denoted by: GPG) with low toxicity that targets tumor cells. This aims to overcome the limitations of current anti-cancer therapy, and explores tumor evaluation and mass coverage status with the targeting peptide, by the use of spectrofluorimetry. The study focuses on monitoring the main actions of the anti-cancer peptide in vivo, with only one set of mouse models based on dual reporter dyes (H₂₂ hepatocarcinoma cell transfected with EGFP, denoted by H₂₂-EGFP, and GPG labeled with fluorescent dye Cy7, denoted by Cy7-GPG, were used thereof). A transplanted tumor mouse model was constructed with H₂₂-EGFP and Cy7-GPG was injected in the tail vein. The imager(Ex = 750 nm) revealed that the number of fluorescent photons in the tumor increased continuously from (3.90±0.260)×10⁶ photons·(s·cm²)^-1 at the 4th hour (the same unit below) to (1.28±0.330)×10⁸ at the 24th hour. Orange fluorescence accumulated on the tumor completely, with no fluorescence accumulation on the tumor in the Cy7 control group. There was no significant change of fluorescent photon number on the tumor as well. At this time, images of the coating Cy7-GPG and actual tumor mass in the same experiment mouse were recorded, at 750 and 488 nm excitation wavelength respectively, indicating that they were of the same size and shape. Subsequently, GPG was injected into the mice in each of the experimental groups every 2 days. The imager(Ex = 488 nm) revealed the tumor had become smaller, while the number of fluorescent photons gradually decreased from (4.15±0.291)×10⁶ on the 2nd day to (4.75±0.283)×10⁴ on the 56th day. However, the condition of the blank control group was just the opposite. The anti-cancer activity of GPG was comparable to cyclophosphamide. However, the latter had more serious toxic side effects on mice. Finally, mice in the GPG efficacy experiment group were injected with Cy7-GPG and were then sacrificed 48 hours later, with the internal organs and tumor masses extracted for recording the number of fluorescent photons using the imager(Ex=750 nm). The experiment showed that GPG had precise targeting, wider coverage, and strong efficacy, with no toxic or side effects on other organs. The fluorescence imaging model constructed using dual reporter dyes used in this paper was utilized to monitor the coverage of targets; it overcame the limitations of the traditional evaluation method for targeting the function of peptide and improved the understanding of the mechanism of drug action. Only one set of mouse models was used in this experiment. Hence the main performance of the targeting peptide was monitored in a simple and cost-effective manner, indicating that GPG has excellent performance and useful application in vivo.
		2020 Vol. 40 (09): 2786-2790 [Abstract] ( 157 ) RICH HTML PDF (1729 KB) ( 72 )

	2791	Correction Methods of Rayleigh Scattering of Three-Dimensional Fluorescence Spectra of Spilled Oil on Sea
		KONG De-ming^{1, 3}, LI Yu-meng¹, CUI Yao-yao^2*, ZHANG Chun-xiang¹, WANG Shu-tao¹
		DOI: 10.3964/j.issn.1000-0593(2020)09-2791-07
		Three-dimensional fluorescence spectroscopy has become a hot topic in the identification of oil spills by many researchers because of its high sensitivity, good selectivity, simple operation and analysis for multi-component mixtures. However, the Rayleigh scattering in the three-dimensional fluorescence spectrum will have a great influence on the accurate detection of the spectrum, so it is of great significance to eliminate the influence of the Rayleigh scattering effectively for the qualitative identification and quantitative analysis of the spectrum. In this paper, the instrument calibration method, background subtraction method, Delaunay triangle interpolation method and Missing Data Recovery (MDR) method were used to correct the Rayleigh scattering in the three-dimensional fluorescence spectrum of the oil spill. Firstly, the seawater SDS micelle solution was used as a solvent, the jet fuel and the lube were mixed according to different relative volume fraction ratios to prepare eight calibration samples and three test samples. Then, the three-dimensional fluorescence spectra of 11 samples were determined by FS920 steady-state fluorescence spectrometer. Moreover, the interference of Rayleigh scattering was eliminated by instrument calibration method, background subtraction method, the Delaunay triangle interpolation method and missing data recovery (MDR) method respectively. Then the kernel consensus diagnosis method was used to estimate the optimal number of components. Finally, the PARAFAC was used to qualitatively identify and quantify the three-dimensional fluorescence spectrum data of the mixed oil samples. The results show that the instrument calibration method using the emission wavelength lag excitation wavelength to eliminate Rayleigh scattering will lose part of the effective spectral information. The background subtraction method cannot completely eliminate the Rayleigh scattering, and there is still scattering interference in the spectrum. The excitation and emission spectra obtained by PARAFAC will be distorted, and the predicted concentration value deviation is large. After the Rayleigh scattering is eliminated by Delaunay triangle interpolation method, the excitation and emission spectra obtained by PARAFAC have a higher agreement with the real spectrum, and the predicted concentration value deviation is small. However,after the Rayleigh scattering is eliminated by MDR, the excitation and emission spectra obtained by PARAFAC analysis have the highest agreement with the real spectrum, and the predicted concentration value deviation is the smallest of these methods, and the sample recovery rate is 98.9% and 100% respectively, the RMSEP is limited to less than 0.130. According to the results of qualitative identification and quantitative analysis, MDR can effectively eliminate the influence of Rayleigh scattering on the basis of ensuring that the original characteristic spectrum is not distorted. It is an ideal method to eliminate Rayleigh scattering in the three-dimensional fluorescence spectrum.
		2020 Vol. 40 (09): 2791-2797 [Abstract] ( 211 ) RICH HTML PDF (5977 KB) ( 56 )

	2798	Three-Dimensional Fluorescence Spectroscopy Coupled With Parallel Factor and Pattern Recognition Algorithm for Characterization and Classification of Petroleum Pollutants
		KONG De-ming^{1, 3}, SONG le-le¹, CUI Yao-yao^2*, ZHANG Chun-xiang¹, WANG Shu-tao¹
		DOI: 10.3964/j.issn.1000-0593(2020)09-2798-06
		With the continuous development of petroleum resources in the ocean, more and more petroleum is leaking into the marine environment. It not only threatens the marine ecological environment but also seriously affects people’s health.Therefore, the rapid and effective detection of petroleum pollutants in the marine environment is of great significance for the protection of the marine ecological environment and human health.Petroleum products contain a large number of polycyclic aromatic hydrocarbons, which have strong fluorescence characteristics.Therefore, fluorescence spectroscopy technology has become one of the important means to detect petroleum pollutants. In this paper, three-dimensional fluorescence spectroscopy combined with parallel factor analysis algorithm and pattern recognition method is used to characterize and classify petroleum pollutants. Firstly, the micelle solution prepared by seawater and sodium dodecyl sulfate (SDS) was used as a solvent to prepare different concentrations of diesel,jet fuel, gasolineand lube solutions, and 80 experimental samples were finally obtained. Then, three-dimensional fluorescence spectra of experimental samples were collected by FLS920 fluorescence spectrometer, and the effect of scattering was removed by using the Delaunay triangle interpolation method. Secondly, the paralleled factor analysis (PARAFAC) algorithm is used to decompose the three-dimensional fluorescence spectrum data after scattering, and the component number is estimated by using the nuclear consistency diagnosis method and residual analysis method. Finally, in order to establish a robust classification model, 80 experimental samples were divided into 60 training set samples, and 20 test set samples by Kennard-Stone algorithm.The K-nearest neighbor (KNN) algorithm, principal component discriminant analysis (PCA-LDA) algorithm and partial least squares discriminant analysis (PLS-DA) algorithm are used to establish the classification model respectively, and sensitivity, specificity and accuracy are used to evaluate the classification effect.The results show that the recognition accuracy of the three classification models is 85%, 90% and 94% respectively. The PLS-DA classification model has the highest recognition accuracy and the best classification effect.Therefore, based on extracting the fluorescence spectrum data of petroleum pollutants by using parallel factor analysis algorithm and combining with the pattern recognition method, the classification of different kinds of oil products can be well studied.In this paper, three-dimensional fluorescence spectroscopy combined with parallel factor analysis algorithm and pattern recognition method is used to detect petroleum pollutants quickly and effectively, which provides a new research idea and an important reference for the rapid detection of petroleum pollutants.
		2020 Vol. 40 (09): 2798-2803 [Abstract] ( 250 ) RICH HTML PDF (2771 KB) ( 81 )

	2804	Near Infrared Spectral Analysis Algorithms for Traceability of Fishmeal Origin
		LI Qing-bo¹, BI Zhi-qi¹, SHI Dong-dong²
		DOI: 10.3964/j.issn.1000-0593(2020)09-2804-05
		Fish meal is a kind of high-protein feed made up of one or more kinds of fish, which occupies a very important position in the aquaculture industry. In order to maintain market order, a method of tracing the origin of the fish meal should be established to identify and analyze the quality of the fish meal more accurately. In this paper, near-infrared spectroscopy (NIRS) and chemometrics are used to trace the origin of fish meal from different habitats quickly and accurately. The support vector machine with radial basis function (RBF-SVM) as the kernel function is used for pattern recognition, and the gray wolf algorithm is used to select the key parameters of RBF-SVM. By simulating the hunting behavior of wolves, a hierarchical system is set up according to the fitness level. The target parameters gradually approximate the movement of encirclement. After each movement, the adaptability is re-evaluated. The prey is finally captured through the iteration of wolf pack rank, and the optimal penalty factor and the radius of the kernel function are searched. Finally, the optimal parameters are used to establish the support vector machine model to trace the origin of fish meal from different origins. Grey Wolf algorithm can improve the speed and accuracy of selecting key parameters in the support vector machine algorithm, and improve the classification accuracy of support vector machine. In this paper, 144 spectra of fish meal samples from four fishmeal producing areas in ZhejiangWenling, Shandong Rongcheng, Shandong Weihai and Liaoning Dalian were obtained. The spectrum ranges from 3 700 to 12 500 cm^-1. The origin of fish meal was traced by the obtained spectra. Seventy percent of the samples from each producing area was randomly selected as the training sample set for modeling and 30 percent as the test sample set. First, the original near infrared spectra are pretreated, and the average spectra of all the collected spectra are calculated by multivariate scattering correction as “ideal spectra”. The other spectra are linearly regressed, and the baseline correction of spectral translation and migration is carried out. The original signal is decomposed by wavelet transform, and the high-frequency signal is thresholded to eliminate the high-frequency noise so as to achieve the smooth denoising effect of the spectral curve. Ten parallel experiments were carried out by support vector machine to reduce error interference, and the classification results were obtained as follows: Zhejiang Wenling, Shandong Rongcheng, Shandong Weihai and Liaoning Dalian were 100%, 98.89%, 96.43% and 97.78%, respectively. Compared with the grid search method, the Improved Grey Wolf algorithm searches for the penalty factor and the radius of the kernel function faster and more accurately, and the classification accuracy is high. It can be seen that the improved grey wolf algorithm’s support vector machine (GWO-SVM) is feasible for tracing the origin of fish meal.
		2020 Vol. 40 (09): 2804-2808 [Abstract] ( 174 ) RICH HTML PDF (1749 KB) ( 83 )

	2809	Study on the Origin Information Authentication Method of Apostichopus Japonicus Based on Amino Acids
		WU Peng^{1, 2}, LI Ying^{1, 2*}, LIU Yu^{2, 3}, CHEN Chen^{1, 2}, RAN Ming-qu^{1, 2}, LI Ya-fang^{1, 2}, ZHAO Xin-da³
		DOI: 10.3964/j.issn.1000-0593(2020)09-2809-06
		The apostichopus japonicus is rich in a variety of active substances, has extremely high medicinal value and economicvalue, and it is an indispensable aquaculture resource for the fishery Industry. There are significant differences in the geographical environment and trophic structure of different producing areas, consequently, the growth cycle and culturing cost of the apostichopus japonicus vary greatly. When consumers buy apostichopus japonicus, they will use the origin information as the primary factor of choice, because the origin of the apostichopus japonicus directly reflects the nutritional value of the food. The price gap between apostichopus japonicus from different producing areas is wide. In the face of the temptation of interest, it is difficult to prevent the occurrence of origin fraud incidents completely. Therefore, a method of apostichopus japonicus origin information authentication with high accuracy, good stability and excellent generalization ability is studied, which effectively protects the vital interests of brand origin practitioners and consumers. Amino acids are the main substances in the nutrient enrichment of apostichopus japonicus. The amino acid characteristics can be used to analyze the composition of primary producers, and as an effective tool for origin information authentication of apostichopus japonicus. Gas Chromatography-Mass Spectrometry (GC-MS) technology produces unique chemical fingerprints for identification of origin information. The 156 samples of the apostichopus japonicus from 9 producing areas were subjected to acid hydrolysis, derivatization and esterification, and finally, the amino acids content and amino acids carbon stable isotope data were determined by GC-MS. Perform a Tukey’s test with a 95% confidence level, and the box-plot were used to check the data distribution, and screen 13 amino acids content and 10 amino acids carbon stable isotope data. Principal component analysis can reduce the data dimension, valuable mine information, aggregate the origin information identification characteristics, and improve the calculation speed and authentication accuracy of the model at the same time. Through cross-validation, the first five principal components were selected as the input of amino acids content and amino acids carbon stable isotope model, and the accumulative contribution rates were 98.727% and 95.982%, respectively. In order to fully exploit the value hidden behind the amino acids data, this paper selected 12 machine learning methods from 8 families, built a total of 24 monomer classifiers, and found the optimal authentication method according to the characteristics of the data itself. The particle swarm optimization algorithm based on genetic crossover factor improvement was used to optimize the model parameters, and the best performance monomer classifier was obtained. The results show that the carbon of the amino acid stable isotope data has better origin authentication characteristics. The support vector machine (Gaussianradial basis as the kernel function) and the k-nearest neighbor algorithms are the best two classification methods. Finally, leverage ensemble learning to bring together the advantages of monomer classifiers, a method for origin information authentication of apostichopus japonicus with fusioning multi-source data processing methods is constructed. The average accuracy of the model is 99.67%. An origin information authentication system for the apostichopus japonicus is established, which provides a simple and feasible mean for the supervision of the competent authorities and consumer rights protection. The occurrence of the apostichopus japonicus origin fraud incidents is effectively prevented and controlled, and the stable and healthy development of the aquaculture industry is ensured.
		2020 Vol. 40 (09): 2809-2814 [Abstract] ( 172 ) RICH HTML PDF (3075 KB) ( 37 )

	2815	Detection of Anthracnose in Camellia Oleifera Based on Laser-Induced Breakdown Spectroscopy
		LIU Yan-de, GAO Xue, JIANG Xiao-gang, GAO Hai-gen, LIN Xiao-dong, ZHANG Yu, ZHENG Yi-lei
		DOI: 10.3964/j.issn.1000-0593(2020)09-2815-06
		The camellia oleifera industry has good economic and ecological benefits, and is highly valued by the state. At present, the anthracnose disease encroaches camellia oleifera tree day by day aggravates, reduces the production seriously, causes the benefit of camellia oleifera industry to suffer directly. So it is necessary to find a fast, accurate and convenient method for anthracnose detection. Laser-Induced Breakdown Spectroscopy (LIBS) is a low-cost, slightly damaged, and no-residue technology that can quickly and real-time detect a variety of ingredients. The qualitative detection method of anthracnose of camellia oleifera was studied by LIBS combined with stoichiometry. The samples were collected from the camellia oleifera planting area. 100 healthy camellia oleifera leaves and 100 anthracnose infected camellia oleifera leaves were collected respectively. The collected blades were micro-treated, namely, the surface stains of the blades were washed repeatedly to remove, then classified, bagged and labeled, and finally LIBS spectrum acquisition experiment was carried out. The experimental equipment was MX2500+ of ocean optics, the LIBS experimental parameters were set as 50 mJ laser energy, and the optimal delay time was 2. Six spectral data were collected from each blade and averaged. The characteristic peak of Si was observed at 251.432 nm of the LIBS spectrum of camellia oleifera leaves, and the characteristic peak of Fe was observed at 252.285, 259.837 and 385.991 nm, and the characteristic peak of Mn was observed at 260.568, 279.482 and 280.108 nm, respectively. Results: the LIBS signals of trace elements Si, Fe, Mn in camellia oleifera leaves are directly related to the health degree of camellia oleifera leaves. In addition, this experiment used LIBS technology combined with MSC spectral pretreatment and PCA classification to classify the two states of camellia oleifera leaf health and anthracnose infection. The contribution rates of PC1, PC2 and PC3 are 80%, 12% and 6% respectively. The establishment of three-dimensional model classification can clearly distinguish the two states of camellia oleifera leaves. At the same time, the PLS-DA model was also used in this paper, and the recognition rate of the model was up to over 90%, which could be used to better classify the two categories of camellia oleifera leaves. The above two stoichiometric methods can distinguish the health and disease of camellia oleifera leaves. The results showed that it was feasible to detect anthrax of camellia oleifera by LIBS. Quantitative detection of trace elements and nutrient elements in camellia oleifera leaves can be carried out by using LIBS technology, which provides a reference for quantitative detection. A new method for rapid detection of anthracnose of camellia oleifera.
		2020 Vol. 40 (09): 2815-2820 [Abstract] ( 179 ) RICH HTML PDF (3173 KB) ( 60 )

	2821	The Content and Kinetics of Sucrose Hydrolysis Were Studied by Raman Spectroscopy
		SU Hui¹, MA Jin-ge¹, XIN Xin², HAN Ying², HUANG Huo-lan¹, HUANG Xiao-cheng¹, YAO Zhi-xiang^1,3*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2821-05
		Online quantitative detection of the content of multivariate components is of great significance for most process analysis. However, in multivariate statistical methods, the spectral similarity and superposition of components will lead to an increase of multivariate statistical error, which is particularly prominent in systems with high similarity components. Chemometrics method based on subspace Angle conversion, calculate the system after the Raman spectral response spectrum noise smoothing, eliminate the differences after calculating the Angle of the pure spectra of the component under test values, a sample content object under test to the percentage, calculated Angle value and component content, the linear relationship between the Angle values of variance and modeling sample components content value relation model, which can realize real-time tracking of component content. Based on sucrose hydrolysis as analysis object, with the aim to the analysis of sucrose content, acquisition of sucrose, fructose, glucose mixture under different concentration ratio of Raman spectrum signal, the first to the second order derivative, eliminate the additive error after smoothing noise is transformed into space vector Angle to eliminate spectral differences between batches, move the window, set up to calculate the Dxi series Angle value of variance, this algorithm of Angle transformation, established the sucrose content and relevant model of spectral Angle value variance, correlation coefficient r of 0.997, model validation available relative error in 3.390%~7.333%, The prediction result of the model is good. Through the Raman spectra of different conditions of sucrose hydrolysis process monitoring, respectively to calculate the response spectra in the process of value variance D_b series Angle, to the model component content, then the reaction rate of different conditions is r, found that the reaction rate changes over the corresponding conditions, both close to the same proportion decreases, and verified the sucrose hydrolysis process is first order reaction, shows that the Raman spectroscopy combined with Angle conversion method can fast track component concentration of sucrose hydrolysis process. Using the data collected by this method, the reaction rate constant K₁ of sucrose hydrolysis at 26.5 ℃ was calculated as 0.031, and the reaction rate constant 0.031 5 was determined by the optical rotation method at the same temperature. The reaction rate constant K₂ at 40 ℃ was calculated to be 0.197 8, and the activation energy E_a=107.1 kJ·mol^-1 was obtained by substituting the Arrhenius equation, which was consistent with the literature value. The results show that the Raman spectrum combined with Angle conversion algorithm can be used to study the dynamics of sucrose hydrolysis process and provide an efficient and fast method for multi-component quantitative analysis.
		2020 Vol. 40 (09): 2821-2825 [Abstract] ( 238 ) RICH HTML PDF (2355 KB) ( 66 )

	2826	Non-Destructive Identification Method of Famous Rice Based on Image and Spectral Features of Hyperspectral Imaging With Convolutional Neural Network
		WENG Shi-zhuang, TANG Pei-pei, ZHANG Xue-yan, XU Chao^*, ZHENG Ling, HUANG Lin-sheng, ZHAO Jin-ling
		DOI: 10.3964/j.issn.1000-0593(2020)09-2826-08
		High-quality rice contains more nutritional value and higher economic value. In order to earn more benefits, some unscrupulous merchants have adulterated high-quality rice or even replaced it with low-quality rice, which has harmed consumer interests and rice trade, and has hurt producers Production motivation. This paper hopes to develop a method for non-destructive identification of high-quality rice based on the features of images and spectra of hyperspectral imaging and deep learning networks. First, hyperspectral images in the 400～1 000 nm range of seven representative rice varieties in China were collected, and the spectra, texture, and shape features of each type of rice were extracted. The spectral features were pre-processed using the multiple scattering correction algorithms to eliminate spectral scattering. Successive projections algorithm (SPA), competitive adaptive weighting algorithm (CARS) and their cascade method (CARS-SPA) were used to select important wavelengths of spectral features. Important variables of shape and texture features were selected using SPA. Finally, convolutional neural network (CNN) was applied to fuse the above-mentioned various features to build rice varieties recognition model, while K-Nearest Neighbors (KNN) and Random Forest (RF) were used for comparison and analysis. The experimental results showed that the classification accuracy of the model constructed using the full spectroscopy reached more than 80%. Among them, KNN had the worst modeling effect and RF had a better effect. In particular, the performance of the CNN model was the best, with training set classification accuracy (ACC_T) of 92.96% and prediction set classification accuracy (ACC_P) of 89.71%. Compared with the full spectroscopy, the spectroscopy of the important wavelengths had worse classification accuracy. In order to further improve the accuracy of rice varieties identification, texture and shape were combined with spectral features, and the optimal result came from the model constructed of important variables of shape and spectroscopy. Among them, ACC_T and ACC_P of KNN were 69% and 67%, respectively. The RF model accuracy corresponded to ACC_T=99.98% and ACC_P=89.10%. The CNN model performed best with ACC_T and ACC_P of 97.19% and 94.55%. In addition, the classification effect of spectroscopy and texture fusion was worse than using only spectroscopy, indicating that texture features weakened the classification result. For classification models, the performance of CNN was significantly better than the two machine learning methods, which could provide better classification results. All in all, the important variables of shape and spectroscopy combined with CNN models could accurately identify high-quality rice varieties. The proposed method can also be applied to the identification of the variety, attribution and grade of other agricultural products.
		2020 Vol. 40 (09): 2826-2833 [Abstract] ( 203 ) RICH HTML PDF (2704 KB) ( 166 )

	2834	Prediction of Eggplant Leaf F_v/F_m Based on Vis-NIR Spectroscopy
		LI Bin^{1, 2, 3}, GAO Pan^{1, 2, 3}, FENG Pan^{1, 2, 3}, CHEN Dan-yan^{1, 2, 3}, ZHANG Hai-hui^{1, 2, 3}, HU Jin^{1, 2, 3*}
		DOI: 10.3964/j.issn.1000-0593(2020)09-2834-06
		Chlorophyll fluorescence parameter F_v/F_m is an important indicator to investigate the effects of stress on plant photosynthesis. Previous studies showed a high linear correlation between vegetation index and F_v/F_m. However, fitting F_v/F_m and vegetation index directly showed insufficient an accuracy. In order to achieve accurate prediction of this parameter, this research took eggplant as the research object, and proposed a F_v/F_m prediction method based on Vis-NIR Spectroscopy. The experiment obtained visible-near infrared spectrum data and F_v/F_m of eggplant leaves in different growth states, Monte Carlo Sampling (MCS) method was used to remove obvious abnormal samples. Three spectral preprocessing methods and 5 characteristic wavelength selection algorithms were adopted for spectral data processing. Partial least squares regression (PLSR) models were built to evaluate these methods. Based on the optimal characteristic wavelength combinations, F_v/F_m prediction models were established by four machine learning algorithms: back propagation (BP) neural network, radial basis function (RBF) neural network, extreme learning machine (ELM), and regression support vector machine (SVR). The effects of the algorithms on the accuracy of the F_v/F_m prediction model were analyzed. Therefore, the optimal combination of the above methods, for F_v/F_m prediction was confirmed. The results were as follows: the spectral reflectance of eggplant leaves decreased significantly with the increase of F_v/F_m, indicating the feasibility of retrieving F_v/F_m by spectral information. Based on 293 sets of experimental samples, two sets of characteristic wavelengths with optimal modeling effect were extracted, which were pre-processed by multivariate scattering correction (MSC) and standard normal variable transformation (SNV) respectively, and screened by the combination use of competitive adaptive reweighted sampling method and successive projections algorithm(CARS+SPA). Among them, the test set determination coefficient (R²) of MSC-CARS-SPA-PLSR and SNV-CARS-SPA-PLSR was 0.896 1 and 0.881 2 respectively. The root means square error was 0.011 8 and 0.012 6. Both showed higher accuracy than the PLSR model of the full spectrum data. Meanwhile, both methods selected 12 characteristic wavelengths, which only accounted for 0. 88% of the full spectrum (1 358). This indicated a small number of wavelengths conducive to model accuracy were selected. Among the machine learning models established by optimal wavelengths, SNV-CARS-SPA-SVR obtained the highest prediction accuracy, with a determination coefficient of 0.911 7 and root mean square error of 0.010 8 the test set. Thus, the SNV-CARS-SPA-SVR modeling method used in this research improved the accuracy of the model and effectively reduced the complexity of the model, providing an implementation method for accurate prediction of F_v/F_m based on the visible-near infrared spectrum. This method can be further applied in rapid and non-destructive detection of crop growth status and early warning of agricultural conditions.
		2020 Vol. 40 (09): 2834-2839 [Abstract] ( 188 ) RICH HTML PDF (2283 KB) ( 70 )

	2840	Research on Nondestructive Testing of Corn Seed Vigor Based on THz-TDS Reflection Imaging
		WU Jing-zhu¹, LI Xiao-qi¹, LIU Cui-ling¹, YU Le¹, SUN Xiao-rong¹, SUN Li-juan²
		DOI: 10.3964/j.issn.1000-0593(2020)09-2840-05
		Sensitive terahertz bands related corn seed vigor were explored using terahertz time domain spectral reflection imaging technique combined with generalized two-dimensional correlation spectroscopy, and simultaneously the qualitative model to judge seed vigor nondestructively was established based on support vector machine and terahertz absorbance spectra. Take Zhongdi 77 (Corn variety) for example in this experiment. Firstly, there are 5 batches of different vigor seeds made by artificial aging (40 ℃, 100% relative humidity) for 0, 1, 2, 3, 4 days. 5 batches of seeds were conducted germination experiments according to GB/T 3543.4—1995 to obtain germination rate. Terahertz spectral images of seed samples were collected by Terapluse 4 000 terahertz time domain system with reflection imaging module. Because the composition of endosperm and embryo of corn seed is significantly different, it is meaningful to explore the correlation between the different tissues (endosperm and embryo) and the vigor in the aging process separately. A series of pretreatments was done to extract the terahertz absorbance spectra of different tissue of corn seed, such as denoising using double Gaussian filtering, image enhancement based on the peak-to-peak differential reconstruction and threshold segmentation. Take the aging days as the disturbance amount, and the generalized two-dimensional correlation spectrum analysis was carried out to the above-mentioned extracted spectra of endosperm and embryo. According to the preliminary analysis of the automatic peak and the cross peak in the synchronous and asynchronous spectra, the terahertz band closely related to seed vigor was mainly concentrated in the 75 and 36 cm^-1 regions, and the spectral information at 75 and 36 cm^-1 had great synergistic change and the change was consistent. Different aging days correspond to different vitality according to the germination experiment. Therefore, five-class support vector machines were built respectively based on endosperm and embryo absorbance spectra to identify seed vigor, and the recognition rate was only 59.34% and 71.28%. The results indicated that the model cannot precisely divide seed vigor to five levels. According to GB4401.1—2008, 85% germination rate was set as the threshold to divide seed vigor to two levels, then binary classifier based on support vector machines were built to distinguish seed vigor. The recognition accuracy of the endosperm and embryo test sets was 88.61% and 91.73% respectively. The recognition rate had improved significantly and the model can basically be used for rapid coarse screening of seed vigor. The experiment result showed terahertz reflection imaging with its rich fingerprint spectrum characteristics and lower energy is expected to be a new and powerful complementary technology to identify seed vigor rapidly and nondestructively.
		2020 Vol. 40 (09): 2840-2844 [Abstract] ( 197 ) RICH HTML PDF (1543 KB) ( 123 )

	2845	Maize Root Phenotypic Detection Based on Thermal Imaging and Root Gap Repair Algorithm
		LU Wei¹, HAN Zhao¹, JIAN Xing-liang¹, Zhou Ji², JIANG Dong³, DING Yan-feng³
		DOI: 10.3964/j.issn.1000-0593(2020)09-2845-06
		Aiming at the problem of incomplete root image information because of blocking by the soil, the paper proposed a root phenotypic method by using thermal image combined with improved Criminisi algorithm for root image repair and studied the relationship between the root phenotype and seed vigor. First, an annular double-layer quartz culture device adapted to maize root configuration was designed to push maize roots to grow along the inner wall of the device, and the maize seeds aged 0, 1, 3 and 6 d were planted in the annular culture device respectively. Base on the significant difference of heat capacity between soil and water, water was used to irrigate the seedling along their stems followed by short-time hot air thermal excitation, and then infrared thermal images were captured based on the temperature difference between the soil and interstitial water flow around the roots. Secondly, the endpoints of the root thermal images after preprocessed were selected and matched for connecting using improved Criminisi algorithm to repair the root image. Finally, different aged-day maize seeds were applied for seeding root phenotyping detection to verify the mentioned method which results show that the proposed thermal infrared imaging method can help to enhance the root phenotypic image information which improves the precision of phenotypic parameters about 0.5%~10% comparedwith color image. The was no significant difference of Root Total Length (RTL) and Root Total Number (RTN) after 1 d aging, but there was remarkable difference of RTL and RTN after 3 and 6 d aging which decreased about 20%~35% and 10%~55% respectively. In general, the maize root phenotypic parameters such as RTN and RTL were significantly negative with the aging-day, which can be used as important index parameters of seed vigor. Furthermore, RTN is more sensitive to impress a seed vigor. Root number of 1 d/3 d and 6 d aging days increasing delayed about 1day and 2 day compared with 0 aging-day seeds respectively. The proposed root phenotypic detection method based on the thermal infrared imaging combined with improved Criminisi algorithm for root image repair can be used in root high throughput non-destructive detection, which has a broad application prospect.
		2020 Vol. 40 (09): 2845-2850 [Abstract] ( 190 ) RICH HTML PDF (3148 KB) ( 46 )

	2851	Identification of Flour Adulteration in White Pepper Powder Using Hyperspectral Imaging
		HUANG Hua¹, ZHU Shi-ping¹, ZHUO Jia-xin¹, LIU Guang-hao¹, ZHU Jie¹, WU Xi-yu²,YU Li-min^3*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2851-05
		White pepper powder is very similar to flour, so it is difficult to distinguish a small amount of flour from white pepper powder by human vision or smell. Hyperspectral imaging technology can not only obtain spectral information but also obtain spatial position information. Therefore, it is possible to predict the content of flour adulteration in white pepper powder and locate the mixing position in white pepper powder by hyperspectral imaging technology. Sixty-two samples are prepared, including 60 samples of pure flour mixed with pure white pepper powder at a ratio of 1% to 60% by weight and a gradient of 1%, in addition, two samples of the pure pepper powder and the pure flour. Each sample was scanned by the hyperspectral image, and a total of 62 hyperspectral data were obtained. Forty-two samples were selected randomly as correction set for partial least squares regression (PLSR) modeling, and the remaining 20 samples were used for prediction set. The pretreatment method of first derivatives was applied to the average spectrum of each sample, and then the PLSR was used to establish a quantitative analysis model for predicting the flour content in white pepper powder. The experimental results show that the root means square error of the correction set is 0.83%, and the root mean square error of the prediction set is 2.73%. The correlation coefficients between the correction set and the prediction set are 0.99 and 0.98 respectively. In order to locate the specific mixing position of flour in the white pepper powder, the Correlation Coefficient Method and the Maximum and Minimum Criterion were proposed. R₁ are used to indicate the correlation coefficient between the sample and the pure flour, and R₂ indicates the correlation coefficient between the sample and the pure white pepper powder. If the location is pure flour, R₁ reaches the maximum and R₂ reaches the minimum. The difference between R₁ and R₂ is calculated to get R, and R is arranged in order from small to large. Using the prediction result of the PLSR regression model as a threshold, the location of R in less than or equal to the threshold value is identified as flour. Then the position of the flour was marked in the adulterated sample so that it could be visually displayed. This research provides a reference for fast, nondestructive and visual identification of white pepper powder adulteration.
		2020 Vol. 40 (09): 2851-2855 [Abstract] ( 191 ) RICH HTML PDF (2576 KB) ( 77 )

	2856	Development of Vehicle-Mounted in-situ Soil Parameters Detector Based on NIR Diffuse Reflection
		ZHOU Peng, LI Min-zan^*, YANG Wei, JI Rong-hua, MENG Chao
		DOI: 10.3964/j.issn.1000-0593(2020)09-2856-06
		Variable fertilizing requires the rapid and in-situ high-accuracy collection of farmland soil nutrients information. However, existing equipment could not meet the needs of field measurement in precision agriculture. Hence, a vehicle-mounted in-situ soil parameters detector was developed based on near-infrared (NIR) diffuse reflection. The detector used a tungsten halogen light source with better illumination stability instead of sunlight to perform soil spectrum detection to improve the adaptability of the instrument to working conditions. A soil total nitrogen measurement extreme learning machine model consisting of seven sensitive wavelengths (1 070, 1 130, 1 245, 1 375, 1 450, 1 550, 1 680 nm) was developed to improve the real-time measurement accuracy. The detector consisted of a mechanical part, optical part and control part. The mechanical part provided platform support for the detector, The optical part was composed of a halogen tungsten light source, a light source adapter flange, a NIR guiding fiber, and a set of detection assembly including an incident light exit end, seven InGaAS photodetectors, and seven single-band filters. The control part realized the collection and processing of the soil measurement signal with a MSP430F149 main control chip module When the detector performed farmland soil nutrients detection, and the tungsten halogen light source transmitted the detection light source to the surface of the detection soil through the NIR guiding fiber and incident light exit end of the detection assembly. The diffuse light from the surface of the detection soil was used to detect soil nutrient parameters. A light source adapter flange at the junction of the tungsten halogen light source and the NIR guiding fiber was designed to minimize the loss of the detection source during transmission. The filter of detection assembly filtered the diffuse light to become a single-band detection light, and the InGaAS photodetector realized photoelectric conversion of the single-band detection light, and the signal processing unit calculated the reflectance at each sensitive wavelength. After the development of the detector was completed, a standard gray board was used as the measurement object to conduct the optical calibration test. The test results showed that the correlation coefficient (R) between the reflectance value of the detector at seven sensitive wavelengths and the reflectance value of the MATRIX-I type Fourier spectrum analyzer had a maximum of 0.997 8 and an average of 0.927 8, which indicated that the detector had higher detection accuracy. In order to further evaluate the detection accuracy of farmland nutrients content using the detector, and the farmland application test of the detector was carried out at the Tongzhou Experimental Station of China Agricultural University. The test results showed that the correlation coefficient (R) between the measured value of soil nutrients content using the detector and the laboratory standard test method were all above 0.90. The test results showed that the vehicle-mounted in-situ soil parameters detector could realize rapid in-situ high-accuracy collection of farmland nutrients information.
		2020 Vol. 40 (09): 2856-2861 [Abstract] ( 251 ) RICH HTML PDF (4572 KB) ( 169 )

	2862	Study on the Relationship Between Black Soil Emissivity Spectrum and Total Potassium Content Based on TASI Thermal Infrared Data
		LI Ming, QIN Kai^*, ZHAO Ning-bo, TIAN Feng, ZHAO Ying-jun
		DOI: 10.3964/j.issn.1000-0593(2020)09-2862-07
		Potassium content in soil is one of the important indicators for evaluating soil nutrient levels. There are few studies using thermal infrared emissivity data to invert potassium, and the model accuracy is low. In this paper, the Thermal Airborne Hyperspectral Imager (TASI) data collected in the Hailun region of Northeast China is used to investigate the relationship between soil emissivity and potassium content in black soil after pretreatment and separation of temperature and emissivity. Compared with the constant multiple stepwise regression and partial least-square regression model, a new stepwise regression method- quadratic multiple stepwise regression is innovatively used to enhance the model. Compared with the constant multiple stepwise regression, more parameters are introduced to establish the model, which can effectively improve the inversion accuracy. It is found that the model which uses effective special selected bands has a higher inversion accuracy to the potassium element and the selected bands are negatively correlated. The bands are 6 (8.602 μm), 11 (9.150 μm), 15 (9.588 μm), and 23 (10.464 μm)and the correlation coefficients are -0.658, -0.673, -0.645, -0.627, respectively. The quadratic multiple stepwise regression model’s RMSE of the training and testing data are 0.027 and 0.032, the decision coefficient R² are 0.667 and 0.82. Compared to the constant multiple stepwise regression model’s RMSE of the training and testing data: 0.031 and 0.031, the decision coefficient R²: 0.569 and 0.78 and the least squares model’s RMSE: 0.033, 0.037, the judgment coefficient R²: 0.45, 0.51, the precisions of evalution indexes have been improved, it is indicated that this method effectively improved the inversion accuracy of the potassium element using the emissivity data. After using the studentized residuals to improve the model to remove the outliers, it is found that the training accuracy is significantly improved but the test accuracy is reduced. Over-fitting the training set data leads to the decline of the model generalization. Therefore, the model is not recommended to improve.
		2020 Vol. 40 (09): 2862-2868 [Abstract] ( 144 ) RICH HTML PDF (5317 KB) ( 49 )

	2869	Mechanism of Fluoride and Arsenic Removal by Ce/γ-Al₂O₃ Based on XRD and FTIR
		ZHANG Hai-yang¹, GAO Bai^{1, 2*}, FAN Hua³ , SHEN Wei¹, LIN Cong-ye¹
		DOI: 10.3964/j.issn.1000-0593(2020)09-2869-06
		The harm caused by fluoride and arsenic in drinking water to public health is a global environmental problem, especially for the high fluorine and arsenic areas without centralized water supply. Compared to other technologies, adsorption to a solid surface is a simple, economical and reliable method for removing arsenic from fluoride. Although the conventional porous adsorbent is stable and inexpensive, the general adsorption amount is not high, and it is difficult to meet the actual needs. Therefore, it is urgent to develop a porous adsorbent which is inexpensive, high-efficiency, and simple in operation flow. The most widely used γ-Al₂O₃ surface has more -OH, the contact liquid has electrical properties, mainly relying on the surface adsorption site to remove fluorine and arsenic, resulting in limited adsorption effect. After modification, the adsorption process of the adsorbent material is complicated, which has the advantages of surface physical adsorption and pore diffusion. Rare earth element (Ce) is the most abundant element in rare earth. It is widely used in catalysts and alloy additives. Its oxide has high adsorption capacity, but the process of preparing granular rare earth oxide is complicated, and it may cause shedding and metal dissolution during use. And other issues. In order to reduce the process and increase the adsorption capacity of the particulate material, the rare metal salt impregnation method is used to avoid the problems of high cost and low mass production caused by complicated process flow. In this study, the porous adsorption material of y-salt Ce(SO₄)₂ loaded γ-Al₂O₃ was prepared by the impregnation method, and the adsorption characteristics of aqueous solution were tested. The adsorption kinetic model and isotherm model were obtained by data fitting to obtain the adsorption process and maximum. The amount of adsorption provides the basis for the adsorption mechanism of Ce/γ-Al₂O₃. The SEM, XRD and FTIR of the adsorption sorbent performance characterization test qualitatively analyze the adsorption force of Ce/γ-Al₂O₃ in addition to fluorine and arsenic, providing Ce/γ-Al₂O₃ Reliable evidence of adsorption mechanism. The results show that the removal of arsenic by fluorine and arsenic in Ce/γ-Al₂O₃ is in line with the pseudo-secondary kinetics and the Langmuir model. The maximum adsorption capacity of arsenic removal by fluoride can reach 47.842 and 18.518 mg·g^-1, respectively. The surface of Ce/γ-Al₂O₃ is smooth, the load is good, and the combination is stable. Ce(Ⅳ) is reduced to Ce(Ⅲ) to form a Ce—O—Al composite. The main body of Ce/γ-Al₂O₃ is amorphous, with a small amount of incompletely developed grain structure and stable surface hydroxyl groups. The combination of XRD and FTIR reflects the phase structure and functional group of Ce/γ-Al₂O₃, which can be used for the identification and analysis of Ce/γ-Al₂O₃, further verifying the adsorption test process and reflecting the adsorption test phenomenon. γ-Al₂O₃ is improved by cerium salt Ce(SO₄)₂ impregnation method, The existence of Al—O and Ce—O, the existence of incomplete grains and the change of crystal structure of surface pore structure are the main controlling factors for the increase of Ce/γ-Al₂O₃ adsorption.
		2020 Vol. 40 (09): 2869-2874 [Abstract] ( 162 ) RICH HTML PDF (3074 KB) ( 249 )

	2875	Frequency Domain Denoising and Regularity Study on XRF Spectra of Soils With Different Lead Concentrations
		FU Ping-jie¹, YANG Ke-ming², LIU Pu-dong^1*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2875-09
		In recent years, the rapid development of global industry and the advancement of urbanization have triggered a series of environmental problems, in which soil heavy metal lead (Pb) pollution has caused widespread concern among researchers. X-ray fluorescence (XRF) spectroscopy has the advantages of low cost, fast analysis speed and suitable for large-area monitoring. It has been widely used in many fields, such as soil pollution detection and ecological environment protection, and it has broad prospects for development. Therefore, it is of great practical significance to fully excavate the XRF spectral information of soil, which can provide solutions for the efficient detection and prevention of soil pollution, the inversion of soil environmental and ecological parameters, and the early warning of heavy metal pollution in mining areas. At present, most of the research on XRF spectroscopy focuses on the accurate evaluation of soil heavy metal concentration measurement and environmental quality assessment, while there are few in-depth studies on the variation of soil XRF spectral characteristics. Time-frequency analysis method can transform complex signals in the time domain into frequency domain space, and detect abnormal information in spectral signals from the angle of the frequency domain. It is an effective method for detecting the change of spectral difference features. Among them, harmonic analysis (HA) could be used for noise removal of electromagnetic signals; smoothed pseudo Wigner-Ville distribution (SPWVD) selects the appropriate basis function in advance, which can highlight the time-frequency local details of the original signal. This paper firstly used HA method to explore the denoising effect of soil XRF spectra with different Pb concentrations, and then used SPWVD of the spectrum to study the local law of denoising XRF spectra of soil samples sampled in the field. The results showed that when the number of harmonic decomposition was 400, soil XRF spectrum de-drying effect was better and saved time, and retained the characteristics of the spectrum. The Pb concentration of soil samples and the distribution of frequency peaks of SPWVD in the XRF spectrum on the 400, 600～700 band sequences had certain regularity. According to this regularity, the Pb concentration of soil in this area could be identified as exceeding the standard, in all in-situ sampled soil, 75% of samples with excessive Pb concentrations could be identified, there was a higher frequency peak near the band sequence of 400 (frequency less than 400 Hz) or 2 very strong frequency peaks (frequency greater than 400 Hz); in all in-situ sampled soil, 79.17% of samples with Pb concentration not exceeded the standard could be identified, there was a strong frequency peak (frequency greater than 400 Hz) near the band sequence of 400, and there were 3 distinct frequency peak distributions between the 600 and 700 band sequences. Accordingly, it was inferred that the XRF spectral characteristic band interval of soil Pb concentration exceeding the standard in this area was 6.42 and 9.42~10.92 keV. Therefore, by introducing the time-frequency analysis method, soil XRF spectral frequency domain analysis and visualization are realized, which provides a new idea for deep mining the characteristics of Pb pollution spectrum and abnormal information.
		2020 Vol. 40 (09): 2875-2883 [Abstract] ( 187 ) RICH HTML PDF (6143 KB) ( 56 )

	2884	X-Ray Fluorescence Spectroscopy Combined With BP Neural Network to Identify Imported Copper Concentrate Origin
		LIU Qian^1，2, QIN Ye-qiong², LIU Shu^2, LI Chen², ZHU Zhi-xiu², MIN Hong², XING Yan-jun^1
		DOI: 10.3964/j.issn.1000-0593(2020)09-2884-07
		Copper concentrate is the basic industrial raw material for smelting copper and its alloys. Imported Copper concentrate with different origins varies in elemental composition and content. Cases of imported copper concentrate falsifying, adulterating and exceeding the standard of harmful elements frequently occur, which endangers national economic security. So it is necessary to establish a rapid identification model of the origin of imported copper concentrates in major importing countries, which is conducive to risk classification and early warning. The research objects of this paper are 280 imported copper concentrate samples from 8 countries in Chile, Peru, Philippines, Spain, Namibia, Iran, Malaysia and Albania. The elemental composition and content of all research samples were determined by wavelength dispersive X-ray fluorescence spectrum standard-less analysis method, and it turned out that elements detected from copper concentrate samples are 53 in total. Among them, we chose 17 elements and conducted a BP neural network prediction model, including O, Mg, Al, Si, P, S, K, Ca, Ti, Fe, Cu, Zn, Mn, As, Mo, Ag, Pb. Moreover, 13 elements including O, Mg, Al, Si, P, S, K, Ca, Cu, Zn, Mo, Ag, Pb were screened out as valid variables by F-score, and the Fisher discriminant analysis prediction model and BP neural network prediction model were established for importing copper concentrate countries respectively. The results of the three prediction models are as follows: the Fisher discriminant analysis model, which uses F-score to screen variables, the recognition accuracy of the model for the modeled sample was 94.2%, the one of cross-validation was 92.8%, and that of the predicted sample reached 96.8%. The accuracy rate of training set, calibration set, validation set, modeling set and prediction sample of BP neural network with input layer of 17 and 13 variables is: 100%, 97.1%, 94.1%, 98.2%, 100% and 100%, 97.1%, 100%, 99.6% and 100%, respectively. Comparing the results of three times of modeling can be seen that the model established by BP neural network has the highest accurate recognition degree after the variables are screened by f-score. This method can not only reduce the input variables of modeling, but also improve the recognition accuracy. Although the wavelength dispersion X-ray fluorescence spectrum standard-less analysis method is a semi-quantitative analysis method, it has the advantages of fast analysis speed and good stability. The country identification of copper concentrate can be realized by using this method combined with F-score screening variables for BP neural network pattern recognition.
		2020 Vol. 40 (09): 2884-2890 [Abstract] ( 155 ) RICH HTML PDF (2216 KB) ( 67 )

	2891	Study on Spectral Characteristics of Laser-Induced Breakdown Copper Alloy at 80 ns Long Pulse Width Under Low Air Pressure
		YUAN Bei, NING Ri-bo, LI Qian, HAN Yan-li, XU Song-ning^*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2891-05
		Laser induced breakdown spectroscopy (LIBS) technology has become an important method to detect the elemental composition and the content of corresponding elements in unknown substances due to its advantages of real-time, fast, multi-element analysis and less damage to samples. Some studies have shown that the hundred nanosecond laser pulse improves the LIBS spectral quality relative to conventional 10 nanosecond pulses, because of prolonging the action time between laser and sample while maintaining an effective breakdown threshold. By reducing the ambient air pressure (to the order of 10⁴ Pa), both the LIBS spectral intensity and the signal-to-back ratio are significantly improved. In order to investigate the effect of low pressure on the spectral characteristics of copper alloy plasma induced by long pulse width laser (100 nanoseconds), a self-developed 80 ns pulse width Nd∶YAG laser (wavelength 1 064 nm, single pulse energy 20~200 mJ) was used as the excitation source. The sample was tin bronze (base element Cu mass percentage 92.9%, low content Fe is 0.007 8%) numbered BYG19431. The ambient pressure was changed by the sample atmosphere control system, and the spectral characteristics of Cu and Fe in the copper alloy matrix under low ambient pressure (1.01×10⁵, 9.6×10⁴, 9.2×10⁴, 8.8×10⁴ and 8.4×10⁴ Pa) were studied, respectively. In the experiment, the repetition rate of the laser pulse is 1 Hz, and each stroke is a fresh surface (the sample position is replaced by a controllable rotating platform in the vacuum chamber), Five spectra with stable pulse energy are selected for each energy and pressure, and the average value is taken as the final experimental result under the current experimental conditions. The real-time monitoring of the laser pulse energy is performed by a transflective ratio 1∶1 beam splitter and an energy meter. It is found that the matrix element line (Cu Ⅰ 324.75 nm) has a relatively high self-absorption phenomenon at low energy (20 mJ, 40 mJ) under normal pressure. At 60 mJ, although the self-absorption effect is improved, the background intensity of the line is increased and the damage of the laser to the sample is increased. In order to further improve the spectral quality under the condition of low-spectrum background and micro-sample damage, the experimental laser energy is 20 mJ. The results show that the self-absorption effect of matrix element Cu is greatly reduced, the ratio of low-content Fe element in the sample increases, the plasma temperature increases, and the spectral line width narrows with the decrease of ambient pressure. When the gas pressure is 8.4×10⁴ Pa, the signal-to-back ratio of matrix element copper (Cu Ⅰ 324.75 nm) and trace element iron (Fe Ⅰ 330.82 nm) increases by 5.31 and 2.43 times, the plasma temperature increases by 21.6%, and the line width of Fe Ⅰ 330.82 nm decreases from 0.29 to 0.21 nm compared with normal pressure, which improves the resolution of the LIBS element line to a certain extent.
		2020 Vol. 40 (09): 2891-2895 [Abstract] ( 139 ) RICH HTML PDF (2454 KB) ( 35 )

	2896	Study on Determination of Se in Geochemical Samples by External Supply H₂-Hydride Generation Atomic Fluorescence Spectrometry
		CHEN Hai-jie^{1, 2}, MA Na^{1, 2}, BAI Jin-feng^{1, 2}, CHEN Da-lei³, GU Xue^{1, 2}, YU Zhao-shui^{1, 2}, SUN Bin-bin^{1, 2}, ZHANG Qin^{1, 2*}
		DOI: 10.3964/j.issn.1000-0593(2020)09-2896-05
		At present, the detection of selenium (Se) is mainly carried out by hydride generation -atomic fluorescence spectroscopy (HG-AFS). It has been found that the sensitivity of Se can be improved by using the interval hydrogen generator to provide the hydrogen (H₂) for HG-AFS. When the H₂ flow rate reaches 80 mL·min^-1 and above, the H₂ provided by the hydrogen generator can ignite the hydrogen flame first, effectively avoiding the influence of ignition early or delay on the measurement, which can improve the precision of the measurement. In geochemical surveys, dozens of elements need to be detection, and they also needs to perform multiple digestions separately. Se, as one of the elements, is digested with nitric acid (HNO₃)-perchloric acid (HClO₄), after the digestion, Se(Ⅵ) will be reduced to Se(Ⅳ) by concentrated hydrochloric acid (HCl). According to DZ/T 0279.3—2016, when using inductively coupled plasma mass spectrometry (ICP-MS) to determine 15 elements such as barium (Ba), beryllium (Be) and bismuth (Bi), the samples are digested with HNO₃-HF-HClO₄ and then dissolved in aqua regia. It was found that Se in geochemical samples is more completely digested with HNO₃-HF-HClO₄, and at large number of chloride ions (Cl^-) contained in aqua regia can reduce Se(Ⅵ) to Se(Ⅳ). Therefore, only one digestion is needed and then detect Se by HG-AFS as well as other 15 elements using ICP-MS separately. Based on the above research results, this method for determining Se in geochemical samples by H₂-HG-AFS has a detection limit of 0.007 mg·kg^-1, and a precision (n=12) of 2.1% to 5.3%. According to the established method, 36 soil and water sediment standard materials were selected to analysis, the relative error was between -13.61% to 16.9%, and most of the errors were within ±10%, which achieved very satisfactory results.
		2020 Vol. 40 (09): 2896-2900 [Abstract] ( 164 ) RICH HTML PDF (888 KB) ( 54 )

	2901	Comparison on the Gemological and Spectral Characteristics of Laos Peach-Blossom Stone and Gaoshan Peach-Blossom Stone
		XU Ya-ting, CHEN Tao^*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2901-07
		Laos Peach-blossom Stone is popular for the similar appearance and quality to that of Gaoshan Peach-blossom Stone, one of the famous varieties in Shoushan Stone. The experimental methods used in this paper include Gem Microscope Observation, X-ray Powder Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR) and Raman spectroscopy (LRM). Laos Peach-blossom Stone samples were tested by the experiment methods mentioned above for the mineral composition, infraredspectral characteristics, Raman spectral characteristics, impurity minerals composition and color genesis, and compared with the characteristics of Gaoshan Peach-blossom Stone. The main mineral composition of Laos Peach-blossom Stone is dickite-kaolinite transition minerals with high to a low degree of crystallization, part of which is dickite with good crystallinity. Laos Peach-blossom Stonecould also contain a small amount of quartz. The characteristic IR absorption peaks of the Laos Peach-blossom Stonein functional group region are at 3 697, 3 653 and 3 621 cm^-1, which related to the O—H stretching vibration. Its mineral composition is disordered dickite-kaolinite transition mineral. The characteristic IR absorption peaks ofthe Gaoshan Peach-blossom Stonein functional group region are at 3 702, 3 653, 3 621 cm^-1. The position and intensity of absorption peak indicate that the mineral composition of Gaoshan Peach-blossom Stoneis ordered-dickite. Laos Peach-blossom Stone and Gaoshan Peach-blossomshare the similar infrared spectral characteristics in the fingerprint area. Their infrared spectrum shows the stretching vibration absorption peak of Si—O and Al—OH at 1 106, 1 034 and 1 006 cm^-1 and the bending vibration caused by Al—OH generates at 937 and 913 cm^-1. The absorption peaks of Si—O—Al stretching vibration appear at 695 and 538 cm^-1as well as the absorption peaks caused by Si—O bending vibration appear at 471 and 430 cm^-1. In the Raman spectrum of the substrate of Laos Peach-blossom Stone, the peaks in the range of 200~1 000 cm^-1reveal the vibration characteristics of the kaolinite group minerals, of which peaks at 202 and 273 cm^-1 belong to the O—H stretching vibration. The peak at 341 cm^-1in the Raman spectrum belongs to Si—O vibration and peaks at 439 and 468 cm^-1 belong to Si—O bending vibration. Peaks of Al—O—Si bending vibration appear at 754 and 800 cm^-1 and peak of O—H bending vibration appear at 921 cm^-1. The O—H stretching vibration period during 3 550~3 750 cm^-1 in the Raman spectrum of Laos Peach-blossom Stone usually shows three peaks similar to that in the high frequency region in the infrared spectrum. The “Peach Blossom” inclusions in the Laos Peach-blossom Stone and Gaoshan Peach-blossom Stone both are hematite, whose typical Raman peaks appear at 225, 296, 411 and 1 318 cm^-1 in the spectrum. Moreover, Gaoshan Peach-blossom Stone also contains anatase, whose typical Raman peaks appear at 145 and 639 cm^-1 in the spectrum. Combined with the magnification observation results and chemical composition analysis, the microcrystalline hematite in Laos Peach-blossom Stone and Gaoshan Peach-blossom Stone make them red.
		2020 Vol. 40 (09): 2901-2907 [Abstract] ( 202 ) RICH HTML PDF (4559 KB) ( 50 )

	2908	Mineralogical and Spectroscopic Characteristics of “Ivory Jade” From Tibet
		ZHENG Jin-yu, CHEN Tao^*, CHEN Qian, LI Meng-yang, YAO Chun-mao
		DOI: 10.3964/j.issn.1000-0593(2020)09-2908-05
		There is a new kind of magnesite jade which is white mostly and some part of it present red color in veins named ivory jade from Tibet. The structure is compact,and color is attractive.Thus it is an ideal stone to carve. The mineralogical and spectroscopic characteristics of the ivory jade are studied with the help of conventional gemological instruments, slice observation, scanning electron microscopy, X-ray powder diffraction (XRD), infrared spectroscopy (IR) and Raman spectroscopy. The gemological testing results show that specific gravity of ivory jade are 2.72~2.94, and reflection index is 1.68 (distant vision technique), strong white fluorescent light is observed under the ultraviolet lamp. There have not extinction characteristics of magnesite exists under the cross-polarized microscope, due to its granularity is too tiny. Quartz vein interpenetrates magnesite and magnesite cracks are filled by hematite. Backscattered electron imaging shows that thematrix is made of magnesite and microcrystalline quartz. There is also some larger size quartz (approximated 25 μm) spread in magnesite. Quartz and hematite are filled in cracks of magnesite as microcrystal. And few calcites are founded in magnesite. XRD testing confirms 3.34 and 4.25 Å which are characteristic diffraction peaks of quartz and 2.74，2.10 and 1.70 Å are characteristics diffraction peaks of magnesite. The FTIR spectrum present 886, 1 453 and 748 cm^-1 which are attribute to carbonate minerals characteristics absorb peaks and the frequency of ν₄（in-plane bending vibration）is inversely proportional to the radius of positive ion, therefore, 748 cm^-1 indicated the sample is magnesite; 1 089，1 165，798，779，694，515 and 465 cm^-1 are distinct peaks of quartz. According to Raman results, the white matrix is the mixture of magnesite and quartz. Transparency vein mineral is made of quartz. In the Raman spectrum of quartz, there are Raman shift 500 cm^-1 which attribute to moganite. In group minerals of quartzite jade, as the main component mineral is α-quartz, if it were contained a great amount of moganite, it should find low crystalline. In another way, the value of I₅₀₀/I₄₆₅ is inversely proportional to crystalline. Thus, both are cryptocrystalline quartz and crystalline in magnesite is lower than in the quartz vein. 1 317，655, 608, 492, 460, 406, 292, 242 and 222 cm^-1 are attributes to Raman shift of hematite. Because there are no biological indication exists, its cause of formation is not related to biogenic. Speculating it may be formed related to weathering-leaching react to ultrabasic.
		2020 Vol. 40 (09): 2908-2912 [Abstract] ( 382 ) RICH HTML PDF (3416 KB) ( 51 )

	2913	Decomposition and Classification of Stellar Spectra Based on t-SNE
		JIANG Bin, ZHAO Zi-liang, WANG Shu-ting, WEI Ji-yu, QU Mei-xia^*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2913-05
		With the development of astronomy and the improvement of telescope observation ability, many large sky survey telescopes have produced petabytes of stellar spectra. Stellar spectra are a kind of complex frequency domain signal, which is usually composed of continuous spectrum and absorption lines. The differences are mainly caused by the effective temperature, surface gravity acceleration and chemical abundance of elements of stars. The automatic classification of stellar spectra is an important part of astronomical data processing and the basis of studying stellar evolution and parameter measurement. The massive stellar spectra require efficient and accurate classification methods. The traditional manual classification methods have the disadvantages of low speed and accuracy, which cannot meet the actual needs of automatic classification of massive stellar spectra. Machine learning algorithms have been widely used in spectra classification. A significant feature of the stellar spectra is the high data dimension. Dimensionality reduction can not only achieve feature extraction, but also reduce the amount of computation, which is the primary task of spectra classification. The traditional linear dimensionality reduction method only reduces the spectra according to the variance, and different types of spectra will cross in the feature space, while manifold learning can produce good classification boundaries to avoid overlap, which is conducive to subsequent classification. In this paper, the distribution of spectra in high dimensional space and the principle of manifold learning to dimensionality reduction of high dimensional linear data are studied. The effects of two dimensionality reduction methods: t-SNE and principal component analysis were compared and the improved k-nearest neighbor algorithm based on the correlation distance of attribute values was used for spectra classification. Python and Scikit-learn were used to implement the algorithm. 12 000 low signal/noise stellar spectra from SDSS were tested and high precision automatic processing and classification of spectral data are realized finally. Experimental results show that the t-SNE method based on manifold learning can restore the low-dimensional manifold structure in high dimensional spectral data. The low-dimensional manifold features in high-dimensional spaces are found and the corresponding embedded mappings are solved. In the process of dimension reduction, the differences between spectral samples of different categories are preserved to the greatest extent. The three-dimensional visualization of the experimental results shows that PCA can lead to the crossover of the distribution of stellar spectra of different categories, while the t-SNE algorithm can produce more obvious category boundaries. The k-nearest neighbor algorithm based on attribute value correlation distance can achieve satisfactory classification accuracy on test data sets after feature extraction. The method used in this paper can also be applied to the automatic classification of massive spectra generated by other telescopes and data mining of rare objects.
		2020 Vol. 40 (09): 2913-2917 [Abstract] ( 192 ) RICH HTML PDF (2120 KB) ( 143 )

	2918	Identification of New and Old Pinus Koraiensis Seeds by Near-Infrared Spectroscopy (NIRs) With t-SNE Dimensionality Reduction
		LI Hong-bo, CAO Jun, JIANG Da-peng, ZHANG Dong-yan^, ZHANG Yi-zhuo^
		DOI: 10.3964/j.issn.1000-0593(2020)09-2918-07
		The new and old characteristics of pinus koraiensis seeds is an important property reflecting the edible value and breeding value. The pinus koraiensis seeds with a short storage period also have high deep processing value. However, it is difficult to distinguish by appearance, weight and texture. At present, traditional biochemical methods are used to detect the chemical properties and germination percentage of pinus koraiensis seeds to judge their new and old quality. It takes a long time to meet the needs of online detection, and improper treatment of chemical reagents can cause environmental pollution. Near-infrared spectroscopy (NIRS) is widely used in the field of food detection and forestry. Therefore, it has practical significance and guiding significance for qualitative analysis of nuts with shells. In this study, near infrared spectroscopy was used to conduct nondestructive testing of pinus koraiensis seeds matured in the current year and in previous years. Firstly, the 120 pinus koraiensis seeds were randomly selected and labeled according to new and old classifications. In order to reduce the leakage of light during the measurement process and make the experimental data more generally, the near-infrared diffuse reflectance spectra of pinus koraiensis seeds samples on the same side were collected uniformly. Then, the original spectrum was pretreated by using a standard normalized variable (SNV), first derivative and Savitzky-Golay (SG) algorithm, so as to reduce the influence caused by human factors and pretreatment in the experiment process, and highlight the characteristic information of the near-infrared spectrum. After that, principal component analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) were used to reduce the dimension of the pretreated data and compare the effect of dimension reduction. Through the visualization of the data and the output of the clustering parameters, a better dimension reduction scheme was obtained by comparison. The non-linear dimensionality reduction method has a good effect in the near-infrared spectral data processing of pinus koraiensis seeds. Therefore, the t-SNE method was used to reduce the dimensionality of the data, and the optimal characteristic variables were obtained. Finally, taking the reduced dimension data as input. Using two-thirds of the sample data as a correction set to establish a support vector machine (SVM) correction model for classification of new and old seeds, and a third of the sample data were used as a validation set to validate the model performance. The results indicate that. The superposition of SNV, first derivative and SG to pretreat the spectrum can effectively eliminate the noise, it makes the absorption peak more obvious. Meanwhile, it also makes the spectral profile clearer and smoother, which is more conducive to the establishment of the later model. The method of t-SNE is used to reduce the data to two-dimension as the input of the classification model, and when the kernel function selects the RBF, the value of K is 5, γ is 82.54 and the penalty coefficient C is 383.12, the SVM classification model has the best classification effect, the accuracy can reach 97.5%, and the average time consumption is 0.02 s. Near-infrared spectroscopy can be used to achieve non-destructive testing of the new and old characteristics of pinus koraiensis seeds.
		2020 Vol. 40 (09): 2918-2924 [Abstract] ( 167 ) RICH HTML PDF (2723 KB) ( 63 )

	2925	*Hyperspectral Discrimination of Different Canopy Colors in Erannis Jacobsoni* Djak-Infested Larch**
		XI Gui-lin^{1, 2}, HUANG Xiao-jun^{1, 2, 3*}, BAO Yu-hai^{1, 2}, BAO Gang^{1, 2}, TONG Si-qin^{1, 2}, Ganbat Dashzebegd⁴, Tsagaantsooj Nanzadd⁴, Altanchimeg Dorjsurene⁵, Enkhnasan Davaadorj⁵, Mungunkhuyag Ariunaad⁴
		DOI: 10.3964/j.issn.1000-0593(2020)09-2925-07
		The outbreak of conifer pests will reduce the water content and chlorophyll content of the conifer trees, cause the forest canopy color to change, and even cause the forest to die, which seriously threatens the health and safety of coniferous forest ecosystem. The remote sensing monitoring of forest canopy color change can be used to evaluate the security of forest ecosystem quickly, so the study of forest canopy color discrimination is very important. Therefore, this study selected three outbreak forest areas (Binder，Ikhtamir and Battsengel) of Erannis Jacobsoni Djak in Khentiy and Hangay province of Mongolia as the experimental areas. The investigation of canopy color change information and spectrum measurement experiment in the process of larch damage was carried out. The hyperspectral characteristics and machine learning algorithm were used to distinguish different colors of Larch canopy. Firstly, through the investigation of forest in the disaster area, the color of the canopy was divided into green, yellow, red and gray. At the same time, according to the different canopy colors of healthy and damaged trees, 66 sample trees were selected from the experimental area, and their spectral canopy were measured. Secondly, the hyperspectral characteristics such as smooth spectral reflectance (SSR), differential spectral reflectance (DSR) and smooth spectral continuous wavelet coefficient (SSR-CWC) were calculated based on the canopy spectral curve of the sample tree, and the sensitivity of these hyperspectral characteristics to different colors on the canopy is revealed by means of variance analysis. Thirdly, the sensitive features of SSR, DSR and SSR-CWC were extracted quickly by using Findpeaks function and continuous projection algorithm pattern (Findpeaks-SPA). At last, the models of different color discrimination of larch tree canopy were constructed by using the random forest classification (RF) and support vector machine classification (SVM) algorithm. And compared with the Fisher discriminant (FD) model, the accuracy of the discriminant models were evaluated. The results show that: ①In multiple wavelengths of visible light, SSR-CWC showed extremely significant sensitivity to different canopy colors. ②The sensitive hyperspectral features can be extracted effectively based on Findpeaks-SAP pattern, which reduces the number of hyperspectral features and improves multicollinearity of the model. ③SSR-CWC is the most potential hyperspectral feature to distinguish different colors on the canopy. The optimal wavelet bases of Daubechies, Biothogonal, Coiflets and Symlets are db9, bior1.5, coif1 and sym4, respectively. Among them, db9-RF (SVMC) reaches the highest overall discrimination accuracy (0.900 0). It is 0.250 0 (0.450 0) and 0.250 0 (0.100 0) higher than the SSR-RF (SVMC) and DSR-RF (SVMC) models. ④The discrimination accuracy of RF and SVMC models based on DSR and SSR-CWC is better than that of FD model, especially db9-RF (SVMC) model, which overall discrimination accuracy and kappa coefficient are 0.150 0 and 0.167 0 higher than db9-FD model, respectively. It can be seen that db9-RF (SVMC) has great potential in different color discrimination of forest canopy, which can provide important reference and practical value for remote sensing monitoring of forest pest severity in forestry and ecological security related departments.
		2020 Vol. 40 (09): 2925-2931 [Abstract] ( 232 ) RICH HTML PDF (3274 KB) ( 60 )

	2932	Determination of Huanghua Pear’s Harvest Time Based on Convolutional Neural Networks by Visible-Near Infrared Spectroscopy
		LIU Hui-jun, WEI Chao-yu, HAN Wen, YAO Yan
		DOI: 10.3964/j.issn.1000-0593(2020)09-2932-05
		The maturity of fruit at harvest time determines its final eating quality. Choosing the optimal harvest time of fruit is one of the key issues to improve fruit quality. Visible/near-infrared spectroscopy technology is suitable for fruit maturity and harvest time determination because of its rapid and non-destructive detection characteristics. Due to the large difference in fruit quality on the tree, traditional chemometric methods require complex spectral pretreatment, and the model is not suitable for different seasons, orchards, etc. In this paper, the discrimination model of Huanghua pear’s harvest time by full convolutional neural networks (CNNs) based on visible/near infrared spectroscopy (Vis/NIR) was proposed. The CNNs was used for spectral feature extraction, and the error backpropagation algorithm combined with the random gradient descent method was used to adjust the connection weights between layers, and output the Logistic regression of harvest time determination, which implemented the end-to-end discrimination of Huanghua pear’s harvest time, and the result was compared with the PLSDA method. The one-dimensional convolutional neural networks included one input layer, two convolution layers, a pooling layer and one Softmax output layer, using cross-entropy as the loss function, and the L2 regularization was used as the regular term to avoid overfitting, without preprocessing. A total of 450 samples were collected for two years. Three hundred samples in the first year constituted training set, 90 samples constituted test set one, and 60 samples in the second year constituted test set two. The results have shown that when the test set and the training set were collected from the same year, correct discrimination rate of PLSDA and CNNs models was 100%, when the test was from different years, correct discrimination rate reduced to 41.67% and 88.33%, respectively. The correlation coefficient and the mutual information of the modes indicated that the CNN model could take advantage of full-spectrum information. Therefore, the CNNs method optimizes convolution kernels through iteration to achieve more flexible preprocessing, which can reduce the difficulty of model training. The established model has good ability of explanation and generalization. The proposed method could be applied in discrimination of fruit’s harvest time.
		2020 Vol. 40 (09): 2932-2936 [Abstract] ( 223 ) RICH HTML PDF (1927 KB) ( 83 )

	2937	Prediction Model of Wood Absolute Dry Density by Near-Infrared Spectroscopy Based on IPSO-BP
		YU Lei, CHEN Jin-hao, LI Long-fei, LI Chao^, ZHANG Yi-zhuo^
		DOI: 10.3964/j.issn.1000-0593(2020)09-2937-06
		Wood density is an important physical property, which determines the mechanical properties of wood. In recent years, as NIR has the advantages of simple, convenient and fast operation, it has already been used in terms of wood density prediction. However, in practical application, the sample sets shortage, spectral characteristics selection and non-linear fitting inaccuracy still not been solved definitely, and the accuracy of wood density prediction model needs to be further improved. Among all the wood density parameters, the absolute dry density of wood is relatively stable, and the measurement results are relatively accurate. In this paper, the prediction of absolute dry density of oak is studied. By collecting spectroscopy information under different moisture content, a non-linear prediction model of absolute drying density suitable for arbitrary moisture content is constructed. The near-infrared optical fiber spectrometer of INSION Company in Germany was selected, and the spectral information of oak samples with different moisture content was collected by SPEC view 7.1 software. Then, the calibration set and prediction set were divided according to 2∶1 using SPXY sample partition method, and multivariate scattering correction, second derivative spectroscopy and S-G smoothing method were used to reduce the influence of scattered light and high-frequency noise; After that, continuous projection algorithm SPA was used to extract effective wavelength information; finally, a BP network (IPSO-BPNN) was used to establish the correlation between near-infrared spectra and oak absolute dry density under different moisture content, which was optimized by a non-linear weighted particle swarm optimization algorithm here. The density and spectral information of 100 samples of oak wood was obtained under absolute drying condition, and the spectral information was collected corresponding to different moisture content. The experimental results show that SPXY guarantees the uniform distribution of calibration samples and improves the generalization ability of the model; Using a combination of MSC, second derivative and S-G convolution can smoothly suppressthe high-frequency noise signal in the original spectrum and make the peak value more prominent;16 characteristic wavelengths were selected by SPA from 117 spectral data. Generally, IPSO-BPNN model has a higher correlation coefficient than SPA-PLS, BP and PSO-BP, own smaller root mean square error. The correlation coefficient of the absolute dry density of oak is 0.938, and the root mean square error is 0.012 9.
		2020 Vol. 40 (09): 2937-2942 [Abstract] ( 166 ) RICH HTML PDF (3486 KB) ( 43 )

	2943	Simple Evaluation of the Degradation State of Archaeological Wood Based on the Infrared Spectroscopy Combined With Thermogravimetry
		YUAN Cheng, ZHAI Sheng-cheng^*, ZHANG Yi-meng, ZHANG Yao-li
		DOI: 10.3964/j.issn.1000-0593(2020)09-2943-08
		The conservation and protection of archaeological wood requires scientific protection schemes based on the knowledge of the main chemical components degradation process, such as the selection of reinforcements, treatment time and temperature. In this paper, four coffin samples were selected, which were excavated from Xuzhou Wanda Han dynasty tombs. These wood samples were identified as four different wood species, namely the hard pine (Pinus sp.), phoebe (Phoebe sp.), catalpa (Catalpa sp.) and zelkova (Zelkova sp.). By Attenuated Total Reflection Fourier Transform IR (ATR-FTIR) and Thermal Gravimetric Analysis (TGA), the chemical properties and fast pyrolysis behavior of the archaeological wood and corresponding sound woods are characterized. The results showed that the absorption peaks of CO stretching vibration from the acetyl group in the infrared spectrum of the archaeological pine, phoebe, catalpa and zelkova almost disappeared near 1 730 cm^-1, while the relative peak intensity of the lignin benzene ring skeleton around 1 500 cm^-1 was increased. These results reflect the serious degradation of hemicellulose in archaeological wood, while lignin preserved better. The absorption peak of acyloxy bond (—COO) at 1 238 cm^-1 in hemicellulose was not found in the samples of archaeological wood holocellulose, but it was detected in the infrared spectra of all modern wood holocellulose except modern phoebe holocellulose, which indicated that the hemicellulose in archaeological wood suffered degradation more seriously than cellulose, and this result indicated that the acyloxy bonds content in phoebe hemicellulose was low. Compared with the acid-insoluble lignin samples of archaeological wood, the intensity of absorption peak near 1 459 cm^-1 (methyl and methylene C-H bending vibration) is stronger than that of archaeological wood, indicating that there are more methyl, methylene and side chains in acid-insoluble lignin of modern wood. In the ATR-FTIR spectra of archaeological acid-insoluble lignin, the absorption peak intensity of lignin in the vicinity of 1 028 cm^-1 is lower than that of modern health wood, indicating that the acid-insoluble lignin of archaeological wood contains few C—O bonds. Comparing the pyrolysis behavior of archaeological wood and referenced wood of different tree species found that the archaeological wood has slower pyrolysis rate, low initial temperature of the rapid pyrolysis stage and higher residue mass. The difference in pyrolysis behavior between archaeological wood and modern wood is mainly related to the massive degradation of holocellulose and the increase of relative lignin content in the archaeological wood. Among the four archaeological wood samples, the residual mass rate of archaeological phoebe is the lowest, which indicates that the relative content of lignin in archaeological phoebe is lower and holocellulose preserved better. Hence, its natural durability is the best among the four tree species. In addition, the pyrolysis rate of archaeological acid-insoluble lignin is slower than reference acid-insoluble lignin due to the low amount of side chains and methoxy groups. The above results show that both infrared spectroscopy and thermogravimetric analysis can be used to analyze the degradation progress of archaeological wood, and provide a scientific basis for the timely conservation of cultural relic.
		2020 Vol. 40 (09): 2943-2950 [Abstract] ( 183 ) RICH HTML PDF (5782 KB) ( 43 )

	2951	Rapid Detection of the Kinetics and Selectivity of Delignification by Sodium Chlorite Based on Raman Spectroscopy
		JIN Ke-xia, JIANG Ze-hui, MA Jian-feng, TIAN Gen-lin, YANG Shu-min, SHANG Li-li, FENG Long, LIU Xing-e^*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2951-06
		Acid-chlorite delignification is the most popular and established laboratory method for the lignin removal from biomass with only trace degradation of the polysaccharides. However, the information on delignification kinetics and selectivity at the cellular level as the reaction proceeds is limited. Raman microscopy can be used to determine the dynamic changes of residual lignin and lignin monomer content in different cells and morphological areas in the delignification process quickly, qualitatively and semi-quantitatively. The average Raman spectra at 1 598, 1 270 and 1 331 cm^-1 in different cells of eucalyptus (angiosperms), coniferous fir (angiosperms) and bamboo (grass) were extracted, which were attributed to lignin, guaiacyl units (G) and syringyl units (S), respectively. It wasfound that the rules of delignification kinetics in the three kinds of wood were consistent, namelya large amount of lignin was rapidly removed at the initial stage of the reaction, and the efficiency of lignin removal decreased with the reaction progress. At the first 0.5 h, the average Raman intensity at 1 598 cm^-1 decreased more than 82%, while only 5%～15% of the average Raman intensity was decreased at the later stage of delignification. Particularly, the bamboo took significantly less delignification time than the wood under the same condition, which the Raman intensity at 1 598 cm^-1 of bamboo fiber decreased more than 88.65% within the first 10 minutes. Meanwhile, lignin removal was highly selective. At the initial stage of reaction, the removal rate of G and S lignin in ray cells was higher than that in the vessel and fiber cells, and in vessel and fiber cells more S lignin were removed than G lignin. In the whole process, the vessel was the most resistant to delignification, followed by ray and less resistant in fiber. In morphologically various areas, therate of lignin removal of the cell corner was the highest, followed by the compound middle lamella, and then the secondary wall of fiber. For lignin monomers, the S units were more prone to being removed than G units. The result showed that Raman spectroscopy could be used to detect the dynamic changes of residual lignin content in different tree species, tissues, cells and lignin units during the gradual delignification process, which could help to further understanding the selectivity and dynamics of delignification.
		2020 Vol. 40 (09): 2951-2956 [Abstract] ( 189 ) RICH HTML PDF (4130 KB) ( 53 )

	2957	The Distribution and Orientation of Cell Wall Components of Moso Bamboo Parenchyma
		FENG Long¹, SUN Cun-ju², BI Wen-si³, REN Zhen-zhen³, LIU Xing-e¹, JIANG Ze-hui¹, MA Jian-feng^1*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2957-05
		Ground parenchyma tissue is regarded as the basic structural units, and its functions are storage. In the present work, Confocal fluorescence microscopy was used to visualize the morphology of separated parenchyma. Moreover, TEM image displayed the concentric layering structure of secondary parenchyma wall and the thickness of the sub-layer ranges from 0.2~0.3 μm. Based on the above findings, the top chemistry of lignin and cellulose in parenchyma was studied by 532 nm in situ confocal Raman spectroscopy. The cell wall morphology of parenchyma was observed by integration the band regions from 2 789~3 000 cm^-1. Due to the limitation of spatial resolution, the secondary wall cannot be divided into sub-layers. Raman imaging obtained from 380 and 1 600 cm^-1 show that the cellulose within the secondary wall of parenchyma was uniform distribution, but the lignin mainly accumulated within the compound middle lamella and its aromaticringconjugated coniferaldehyde and sinapaldehyde displayed the same distribution pattern. Moreover, the distribution pattern of hydrocinnamic acids, which are attached to lignin and hemicelluloses via ester and ether bonds, was also heterogeneous. The ratio of Raman band intensity revealed that the cellulose molecular chain within the parenchyma and narrow layer of fiber wall was more parallel to the cell axis compared to the broad layer of fiber. The above results will deepen our understanding of ultrastructure, cell wall topochemistry and molecular orientation of bamboo parenchyma. It will provide theoretical instruction for the highly efficient and precise utilization of bamboo resources in the further.
		2020 Vol. 40 (09): 2957-2961 [Abstract] ( 260 ) RICH HTML PDF (2897 KB) ( 48 )

	2962	Feasibility Study on Quantitative Analysis of Ancient Lacquer Films by Infrared Spectroscopy
		XIAO Qing, WEI Shu-ya^*, FU Ying-chun
		DOI: 10.3964/j.issn.1000-0593(2020)09-2962-06
		Lacquerware is an important part of Chinese tangible cultural heritage, which runs through the whole Chinese history. In order to improve the performance of raw lacquer, ancient people mixed drying oil with raw lacquer.The proportion ofdrying oil directly affected the propertiesof the lacquer film. Unfortunately, due to the lack of relevant historical literature and the aging degradation of ancient lacquerwares from archaeological excavations, it is difficult to determine the materials used in the lacquer film quantitatively. Therefore, in order to understand the ratio of drying oil to raw lacquer usedin the ancient lacquer making process, it is necessary to establish a method for quantitative analysis of the ancient lacquer film oil/lacquer ratio. In this study, the ancient lacquer film was quantitatively analyzed by FTIR-ATR and NIR through standard samples. The results showed that FTIR-ATR could not achieve the purpose of quantitative analysis when the lacquer film seriously aged. The PLS model established by near-infrared spectroscopy combined with chemometrics could quickly and non-destructively analyze the oil/raw lacquer ratio of ancient lacquer film. The results show that the PLS model has good stability and predictability, with lower the root mean square error of calibration (RMSEC), root means square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and correlation coefficients of R_c, R_p, R_v above 0.99. The method was applied to analyze ancient lacquerwares. The relative content of the drying oil contained in the ancient lacquer film was calculated by the PLS quantitative model, and the different proportions of oil/rawlacquer used in lacquer making in different times and regions were determined. It provides a scientific basis for interpreting the "oil and lacquer" technology of ancient China and provides scientific support for the conservation and restoration of lacquer artifacts.
		2020 Vol. 40 (09): 2962-2967 [Abstract] ( 179 ) RICH HTML PDF (1953 KB) ( 70 )

	2968	Evaluating the Validity of 2D Images in Reflecting the 3D Structure of a Symmetrical Cone Flame Using Orthogonal Planar Laser-Induced Fluorescence
		LI Hong, GAO Qiang, LI Xiao-feng, ZHANG Da-yuan, LI Bo^*, YAO Ming-fa, LI Zhong-shan
		DOI: 10.3964/j.issn.1000-0593(2020)09-2968-06
		By analyzing planar laser-induced fluorescence (PLIF) images, the key parameters such as flame surface density (Σ), flame brush thickness and turbulent combustion velocity in the turbulent flame can be obtained, and the three-dimensional (3D) flame structure can be reconstructed based on two-dimensional (2D) PLIF images and the key parameters. It is not clear, however, whether the 2D PLIF images can accurately reflect the 3D flame structure. In this study, the 2D OH distribution of methane/air turbulent premixed flames was measured using orthogonal PLIF in both horizontal plane (perpendicular to the flame propagation direction)and vertical plane (parallel to the flame propagation direction), and the Σ was calculated by analyzing the OH-PLIF images. The Σ in the two planes were obtained under various conditions, i.e., different exit velocities, different locations, and different equivalence ratios. The results showed that the Σ in the vertical plane was smaller than that in the horizontal plane under almost all the conditions, and the difference in Σ between the two planes is decided by the burner exit velocity, the location, and the equivalence ratio. This phenomenon shows that the 2D PLIF technique has some limitations in accurately reflecting the 3D flame structure.
		2020 Vol. 40 (09): 2968-2973 [Abstract] ( 142 ) RICH HTML PDF (4012 KB) ( 64 )

	2974	The Rapid Detection of La and Ce in Steel Materials by Portable EDXRF
		NI Zi-yue¹, CHENG Da-wei², LIU Ming-bo², HAN Bing², LI Xiao-jia^1,2, CHEN Ji-wen³
		DOI: 10.3964/j.issn.1000-0593(2020)09-2974-07
		Rare earth elements, with their unique electronic structure and active chemical properties, are important additives in the metallurgical industry and play an important role in many fields. Not only can rare earth additives be used as deoxidizer and desulfurized to purify the molten steel, but also have metamorphism and alloying effects on the steel, which can improve the structure and performance of the steel materials. However, only with a certain range can the addition of rare earth elements in steel materials shows good properties. Although inductively coupled plasma mass spectrometry and inductively coupled plasma atomic emission spectrometry are usually used to detect rare earth elements in steel materials, which require sample digestion and tedious operations for a long test period. In this study, portable energy dispersive X-ray fluorescence spectrometry was used to realize the rapid detection of lanthanum and cerium in steel materials, and the whole weight of the instrument is less than 10 kg, which is convenient to test on-site. Compared with selecting L series lines for analysis and testing in the traditional portable instrument, a high-power X-ray tube is used to excite the K series spectral lines for rare earth elements, which can increase the intensity of analyzed peaks and avoid overlap interference of other common components in steel materials. The intensity and peak-to-background ratio were studied at different X-ray tube current and voltage when the measured time was set at 120s, and finally, the optimized parameters were chosen as 800 μA and 65 kV for irradiating samples. The calibration curves were drawn with reference materials, and the linear correlation coefficients of lanthanum and cerium were 0.999 2 and 0.998 8 respectively, after the correction of matrix effect by the intensity of the background. Reference samples of GBW01135 were chosen to calculate the detection limit and quantitation limit because both the content of La and Ce were of low content. Moreover,the detection limit of La and Ce was 0.001 1% and 0.000 5% respectively, and the quantitation limit was 0.003 8% and 0.001 6%, which satisfied the requirement of the actual test. The stability of the test was studied by 11 consecutive measurements for the sample of GBW01132a, and the relative standard deviation was 2.42% and 2.00% for La and Ce. Furthermore,the accuracy of the test results was also studied by testing multiple samples and comparing with reference values. The results showed that the relative error of the results were less than 20% except for one sample which was below the detection limit, and the relative error of more than 70% samples were less than 10%. The energy dispersive X-ray fluorescence spectrometry can realize the rapid detection of rare earth elements in steel materials, and the samples can be directly tested with simply polishing pretreatment, which is of certain significance for further research on the properties of steel materials.
		2020 Vol. 40 (09): 2974-2980 [Abstract] ( 157 ) RICH HTML PDF (1745 KB) ( 187 )

	2981	Study on the Predication Modeling of COD for Water Based on UV-VIS Spectroscopy and CNN Algorithm of Deep Learning
		JIA Wen-shen^1,2,4,5, ZHANG Heng-zhi², MA Jie², LIANG Gang^1,4,5, WANG Ji-hua^1,4,5, LIU Xin^3*
		DOI: 10.3964/j.issn.1000-0593(2020)09-2981-08
		Water is vital for human life, and the quality of water is directly related to people’s quality of life. At present, research into chemical oxygen demand (COD) methods for determining water quality is mainly focused on spectral data preprocessing and spectral feature extraction, with few studies considering spectral data modeling methods. Convolutional neural networks (CNN) are known to have strong feature extraction and feature mapping abilities. Thus, in this study, a CNN is combined with UV-visible spectroscopy to establish a COD prediction model. The Savitzky-Golay smoothing filter is applied to remove noise interference, and the spectral data are then input to the CNN model. The features of the spectrum data are extracted through the convolution layer, the spatial dimensions are reduced in the pooling layer, and the global features are mapped in the fully connected layer. The model is trained using the ReLU activation function and the Adam optimizer. A series of experiments show that the CNN model has a strong ability to predict COD in water, with a high prediction accuracy and good fit to the regression curve. A comparison with other models indicates that the proposed CNN model gives the smallest RMSEP and MAE, the largest -R², and the best fitting effect. It is found that the model has strong generalization ability through the evaluation effect of the training samples. To counter the inaccuracy of the predicted results caused by the peak shift of the absorption spectrum, a regression model based on a strengthened CNN (CNNs) is also developed. After denoising, the spectral data can be divided into three categories according to the different characteristics of absorption peaks, and the corresponding CNN regression model is input respectively for prediction. When the corresponding regression model is applied, the experimental results show that the sectional CNNs model outperforms our original CNN model in terms of fitting, prediction precision, determination coefficient, and error. Not only does R² increase significantly, reaching 0.999 1, but also the MAE and RMSEP of the test samples also reduced to 2.314 3and 3.874 5, respectively, which were reduced by 25.9% and 21.33% compared with out original CNN. Performance testing of the prediction model, indicates that the detection limit is 0.28 mg·L^-1and the measurement range is 2.8~500 mg·L^-1. This paper describes an innovative combination of a CNN with spectral analysis and reports our pioneering ideas on the application of spectral analysis in the field of water quality detection.
		2020 Vol. 40 (09): 2981-2988 [Abstract] ( 301 ) RICH HTML PDF (3276 KB) ( 322 )

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